text:
## The creep factor: How to think about big data and privacy

There was a great passage in Alexis Madrigal’s recent interview with Gibu Thomas, who runs innovation at Walmart:

“Our philosophy is pretty simple: When we use data, be transparent to the customers so that they can know what’s going on. There’s a clear opt-out mechanism. And, more important, the value equation has to be there. If we save them money or remind them of something they might need, no one says, “Wait, how did you get that data?” or “Why are you using that data?” They say, “Thank you!” I think we all know where the creep factor comes in, intuitively. Do unto others as you want to be done to you, right?”

This notion of “the creep factor” seems fairly central as we think about the future of privacy regulation. When companies use our data for our benefit, we know it and we are grateful for it.  We happily give up our location data to Google so they can give us directions, or to Yelp or Foursquare so they can help us find the best place to eat nearby. We don’t even mind when they keep that data if it helps them make better recommendations in future. Sure, Google, I’d love it if you can do a better job predicting how long it will take me to get to work at rush hour!  And yes, I don’t mind that you are using my search and browsing habits to give me better search results.  In fact, I’d complain if someone took away that data and I suddenly found that my search results just weren’t as good as they used to be!

But we also know when companies use our data against us, or sell it on to people who do not have our best interests in mind. Read more…

## A concrete approach to learning how to program

### A solid foundation on which more meaningful learning can happen

As someone who has previously taught computer programming for nearly a decade, I’m often asked questions that involve “what’s the best way to go about learning to program computers,” or “what’s the best way to get a software engineering job,” or “how can I learn to build mobile or web apps?”

Most of the readers of this blog have probably faced the same question at some point in their career. How did you answer it? I’ve seen many different responses: “come up with an idea for an app and build it,” or “get a computer science degree,” or “go read The Little Schemer,” or “join an open-source project that excites you,” or “learn Ruby on Rails.”

The interesting thing about these responses is that, for the most part, they can be classified into one of two categories: top-down approaches or bottom-up approaches. Top-down approaches are informed by the opinion that it’s better to be thrown in the middle of an application or a framework which encourages the learner to piece together knowledge in that context. Many books and online tutorials use an explicit top-down approach, often starting with the basics of a popular methodology, framework or technology. The most visible example of this are books on Ruby on Rails — they almost always inevitably begin with a description of the Model-View-Controller design pattern, but defer the incredible number of non-obvious ideas that make it up (Object-Oriented Programming, for instance).

On the other hand, a bottom-up approach starts with the basics/fundamentals of programming and then slowly builds your knowledge over time. In contrast to top-down approaches, bottom-up approaches try to minimize the number of these non-obvious ideas that the learner has to take for granted. Khan Academy and Code Academy are two examples of online sites that use a bottom-up approach to teaching programming. For the most part, they completely leave out any specific framework and focus on fundamentals of programming.

## The new PHP

### PHP's experiencing a renaissance, with improvements and new standards

The programming language many love to hate is experiencing a renaissance. This is not your parents’ PHP. The new PHP is a more mature language with community standards, a growing affinity for interoperable components, and a passionate movement to improve performance. If you have bypassed PHP for alternative languages, or if you are a PHP veteran unaware of recent changes, you owe it to yourself to give PHP a second look.

## Language Features

PHP 5.5 (the latest stable build as of this writing) has made major progress from earlier versions. Recent PHP releases contain powerful new features and helpful developer tools, such as a built-in web server, generators for simpler iteration, and namespaces. With PHP 5.4, traits were introduced (a la Scala or Perl) to allow code reuse in single inheritance languages, as well as closures, which allow you to code PHP in a functional style. Other important features include the built-in FastCGI process manager and phpdbg debugger, and a new password hashing API that makes it easy to hash and securely manage passwords in PHP.

## Parsing HTML with Perl

### Efficiently manipulate documents on the Web

The need to extract interesting bits of an HTML document comes up often enough that by now we have all seen many ways of doing it wrong and some ways of doing it right for some values of “right”.

One might think that one of the most fascinating answers on Stackoverflow has put an end to the desire to parse HTML using regular expressions, but time and again such a desire proves too tempting.

Let’s say you want to check all the links on a page to identify stale ones, using regular expressions:

In this self-contained example, I put a small document in the __DATA__ section. This example corresponds to a situation where the maintainer of the page commented out a previously broken link, and replaced it with the correct link.

When run, this script produces the output:

## Web application development is different (and better)

### On both front and back end, the Web challenges conventional wisdom

The Web became the most ubiquitous distributed application system because it didn’t have to think of itself as a programming environment. Almost every day I see comments or complaints from programmers (even brilliant programmers) muttering about how many strange and inferior parts they have to deal with, how they’d like to fix a historical accident by ripping out HTML completely and replacing it with Canvas, and how separation of concerns is an inconvenience. Everything should be JavaScript.

(Apologies to Tom Dale, who tweeted a perfect series of counterpoints just as I was writing. He has visions of rebuilding the rendering stack in JavaScript, but those tweets are not unusual opinions.)

The Web is different, and I can see why programmers might have little tolerance for the paths it chose, but this time the programmers are wrong. It’s not that the Web is perfect – it certainly has glitches. It’s not that success means something is better. Many terrible things have found broad audiences, and there are infinite levels to the Worse is Better conversations. And of course, the Web doesn’t solve every programming need. Many problems just don’t fit, and that’s fine.

So why is the Web better?

format: plain


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# Not getting right alias name with Cakephp

I am new on CakePHP and facing a problem with SQL Alias. I want to make an Excel file to generate a report from the MySQL database.

``````/**
* Generate transaction report
*
* @param none
*
* @return void
*/
public function generateReport()
{

//Getting data from the database to generate the report in Excel file
\$data = \$this->AchTransaction->find('all', array(
//'conditions' => \$searchCondition,
'fields' => array(
"payee_name AS PAYEENAME",
'reference AS REFERENCE',
'reference2 AS REFERENCE2',
'currency AS CURRENCY',
'amount AS PAYOUTAMOUNT',
'bank_name AS BANKNAME',
'bank_branch_name AS BANKBRANCHNAME',
'bank_country AS BANKCOUNTRY',
'account_name AS ACCOUNTNAME',
'account_class AS  ACCOUNTCLASS',
'account_type AS ACCOUNTTYPE',
'bank_routing_number AS BANKROUTING',
'account_number AS ACCOUNT',
'beneficiary_city AS BENEFICIARYCITY',
'beneficiary_state AS BENEFICIARYSTATE',
'beneficiary_postcode AS BENEFICIARYZIPCODE',
'beneficiary_country AS BENEFICIARYCOUNTRY',
'note as NOTE'
),
'order' => array('AchTransaction.id DESC')
));

// Generating Excel file
\$this->Common->generateReport(\$data);
}
``````

I am able to generate the Excel but having only one problem in alias, As you can see

``````"payee_name AS PAYEENAME",
``````

Here the `payee_name` is the name of column or field in MySQL table, and I want this as `PAYEE NAME`, but when I write:

``````"payee_name AS PAYEE NAME",
``````

I am getting this MySQL error:

``````Database Error

Error: SQLSTATE[42S22]: Column not found: 1054 Unknown column 'payee_name AS PAYEE NAME' in 'field list'

SQL Query: SELECT `payee_name AS PAYEE NAME`, `reference` AS `REFERENCE`, `reference2` AS `REFERENCE2`, `currency` AS `CURRENCY`, `amount` AS `PAYOUTAMOUNT`, `bank_name` AS `BANKNAME`, `bank_branch_name` AS `BANKBRANCHNAME`, `bank_address` AS `BANKADDRESS`, `bank_country` AS `BANKCOUNTRY`, `account_name` AS `ACCOUNTNAME`, `account_class` AS `ACCOUNTCLASS`, `account_type` AS `ACCOUNTTYPE`, `bank_routing_number` AS `BANKROUTING`, `account_number` AS `ACCOUNT`, `beneficiary_address` AS `BENEFICIARYADDRESS`, `beneficiary_city` AS `BENEFICIARYCITY`, `beneficiary_state` AS `BENEFICIARYSTATE`, `beneficiary_postcode` AS `BENEFICIARYZIPCODE`, `beneficiary_country` AS `BENEFICIARYCOUNTRY`, `note` AS `NOTE` FROM `aramor_payments`.`ach_transactions` AS `AchTransaction` WHERE 1 = 1 ORDER BY `AchTransaction`.`id` DESC

Notice: If you want to customize this error message, create app\View\Errors\pdo_error.ctp
Stack Trace

CORE\Cake\Model\Datasource\DboSource.php line 461 → PDOStatement->execute(array)
CORE\Cake\Model\Datasource\DboSource.php line 427 → DboSource->_execute(string, array)
CORE\Cake\Model\Datasource\DboSource.php line 671 → DboSource->execute(string, array, array)
CORE\Cake\Model\Datasource\DboSource.php line 1082 → DboSource->fetchAll(string, boolean)
CORE\Cake\Model\Model.php line 2631 → DboSource->read(AchTransaction, array)
APP\Controller\AchtransactionsController.php line 353 → Model->find(string, array)
[internal function] → AchtransactionsController->generateReport()
CORE\Cake\Controller\Controller.php line 485 → ReflectionMethod->invokeArgs(AchtransactionsController, array)
CORE\Cake\Routing\Dispatcher.php line 186 → Controller->invokeAction(CakeRequest)
CORE\Cake\Routing\Dispatcher.php line 161 → Dispatcher->_invoke(AchtransactionsController, CakeRequest, CakeResponse)
APP\webroot\index.php line 92 → Dispatcher->dispatch(CakeRequest, CakeResponse)
``````

Can you please help me to get the name of `payee_name` field as `PAYEE NAME`?

-
You can use square brackets with the column containing spaces within the back-quotes like `[Payee Name]` –  Arun Jain Sep 2 '12 at 15:51

Change

``````"payee_name AS PAYEE NAME",
``````

to

``````"`payee_name` AS `PAYEE NAME`"
``````

Another option is using `virtualFields`

``````\$this->AchTransaction->virtualFields['PAYEE NAME'] = 'payee_name';
array('fields' => array('PAYEE NAME', 'etc'))
``````
-
Thanks dude..... :) –  Ram Sep 12 '12 at 18:03
You should accept it as an answer if that solved your problem @Ram –  techie_28 Dec 8 '12 at 6:17


text:
# Question No.8

A cylindrical buoy 1.35 m in diameter and 1.8 m high has a mass of 770 kg. Show that it will not float with its axis vertical in sea water  ( S.G = 1.025 ). What minimum pull should be applied to a chain attached to the center of the base to keep it vertical ?

### Given :

• $S.Gseawater=1.025$

### Solution :

$First, we should calculate the height of the immersed part of the buoy h :$

$W=FB$

$770=1.025∗1000∗π4∗1.352∗h$

Second , check the stability of the floating body :

$MG=IminVimmersed−GB$

$Vimmersed=Ah$

$Since the body is uniform →$ $GB=H2−h2$

$Imin=π64D4$

$Since MG is negative, therefore the buoy is unstable !$

The buoy will not float with its axis vertical in sea water !

To find the minimum pull that should be applied to a chain attached to the center of the base to keep it vertical :

$W+T=FB$

$770+T=γseawaterAh'$

$770+T=1.025∗1000∗π4∗1.352∗h'$

$770+T=1466.43h'$ $→$ $1$

$MG'=IminVimmersed'−GB'=0$ $→$ $IminVimmersed'=GB'$

$Vimmersed'=Ah'$

$Vimmersed'=π4∗1.352∗h'=1.43h'$

$Imin=π64D4$ $→$

$W∗H2+T∗0=(W+T)∗x$

$770∗1.82=(770+T)∗x$

$x=693770+T$

$GB'=x−h'2$

$GB'=693770+T−h'2$

$IminVimmersed'=GB'$

$0.1631.43h'=693770+T−h'2$

From 1 :

$0.1631.43h'=6931466.43h'−h'2$ $∗h'$

$0.114=0.473−0.5h'2$ $→$ $h'2=0.718$

From 1 :

$770+T=1466.43∗0.847$ $→$ $T=472 kg$


text:
# Consider the following statements:The causes of error in the measurement of temperature using a thermistor are1. Self heating2. Poor sensitivity3. Non-linear characteristicsWhich of these statements are correct?

This question was previously asked in
ESE Electrical 2013 Paper 1: Official Paper
View all UPSC IES Papers >
1. 1, 2 and 3
2. 1 and 2 only
3. 2 and 3 only
4. 1 and 3 only

Option 4 : 1 and 3 only
Free
CT 3: Building Materials
3014
10 Questions 20 Marks 12 Mins

## Detailed Solution

The thermistor is temperature-dependent. It is a nonlinear resistance thermometer. It is a highly sensitive element.

$${R_{T1}} = {R_{T0}}{e^{\beta \left( {\frac{1}{{{T_1}}} - \frac{1}{{{T_0}}}} \right)}}$$

RT1 is the resistance at temperature T1,

RT0 is the resistance at temperature T0, β = temperature constant

As the temperature increases, there are many bond breaks and more electrons/holes that contributor to the conduction therefore resistance of the thermistor decreases (producing a negative temperature coefficient). By this process, we can measure temperature.


text:
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## Original Article

Split Viewer

J Korean Dysphagia Soc 2021; 11(2): 128-136

Published online July 30, 2021 https://doi.org/10.34160/jkds.2021.11.2.007

## Reliability of Suprahyoid and Infrahyoid Electromyographic Measurements during Swallowing in Healthy Subjects

Myung Woo Park, M.D.1, Dongheon Lee, Ph.D.2, Han Gil Seo, M.D., Ph.D.1,7, Tai Ryoon Han, M.D., Ph.D.1, Jung Chan Lee, Ph.D.3,4,5, Hee Chan Kim, Ph.D.4,5,6, Byung-Mo Oh, M.D., Ph.D.1,7,8,9,10

1Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, 2Biomedical Research Institute, Seoul National University Hospital, Seoul, 3Department of Biomedical Engineering, Seoul National University Hospital, Seoul, 4Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 5Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, 6Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 7Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, 8Institute on Aging, Seoul National University, Seoul, 9National Traffic Injury Rehabilitation Hospital, Yangpyeong, 10Neuroscience Research Institute, Seoul National University College of Medicine, Seoul, Korea

Correspondence to:Byung-Mo Oh, Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: +82-2-2072-2619, Fax: +82-2-6072-5244
E-mail: moya1@snu.ac.kr

Received: March 8, 2021; Revised: March 9, 2021; Accepted: June 6, 2021

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

### Abstract

Objective: To evaluate the reliability of suprahyoid and infrahyoid electromyography (EMG) measurement during swallowing.
Methods: In all, 10 healthy volunteers were evaluated for the following surface EMG (sEMG) parameters in the suprahyoid and infrahyoid muscles during swallowing: onset latency, offset latency, duration, peak latency, maximal amplitude during swallowing, and the area under curve (AUC) of the rectified EMG signal. The sEMG was recorded while the participants swallowed five times each of the four fluid volumes (saliva, 2 ml, 5 ml, and 20 ml of water), totaling to 20 swallows. Moreover, the intra-participant variability per parameter was evaluated using the coefficient of variation (CV).
Results: Suprahyoid muscles were activated 0.095 s (95% CI, 0.062-0.128) earlier than the infrahyoid muscles. Maximal amplitudes during the 20 ml swallow were 17.484 (−1.543-36.512) and 13.490 (1.254-25.727) μV higher than values obtained during the 2 ml swallow in the suprahyoid and infrahyoid muscles, respectively. Furthermore, the AUC of the rectified EMG signal increased with the volume of swallow in both muscle groups (P=0.003, suprahyoid; P<0.002, infrahyoid). The intra-individual variabilities of offset latency, duration, and maximal amplitude were relatively low (<30% CV) in both muscle groups with respect to other parameters. The assessment of each parameter using EMG was highly reliable, with an intraclass correlation coefficient of ＞0.8.
Conclusion: Among the variable sEMG parameters assessed, the offset latency, duration, and maximal amplitude were the least variable. Although reliability on the rater side showed good results, the swallow-to-swallow variability of the parameters need to be considered in swallowing studies using sEMG

Keywords: Electromyography, Deglutition disorders, Reliability of results

### INTRODUCTION

Swallowing is a complex process involving a coordinated activation of many muscles, including the oral, pharyngeal, and laryngeal muscles, at different levels of the central nervous system from the cerebral cortex to the medulla oblongata1. The prevalence of dysphagia is very high in elderly patients, and it affects more than 30% of patients with stroke, 60-80% of patients with neurodegenerative disease, and more than 51% of institutionalized elderly patients2. Physicians in various fields frequently encounter patients with these disorders; therefore, a simple and noninvasive screening tool is required for such patients. Currently, videofluoroscopic swallowing study (VFSS) is the gold standard for evaluating patients with dysphagia; however, it uses radiation, requires radiologic equipment and personnel, and is expensive3. With the increasing need for a simple and rapid screening tool for dysphagia, surface electro-myography (sEMG) could be considered as a valuable method for evaluating dysphagia because of its noninvasive, radiation-free, inexpensive, and time- saving features. Various studies have used sEMG to evaluate the physiology of swallowing and the pathophysiological mechanisms involved in patients with dysphagia4-7. Despite the advantages of sEMG, there is a paucity of studies with normative data for normal swallowing and the reliability of sEMG for swallowing function evaluation.

This is a preliminary study evaluating the reliability of sEMG and investigating normative data for suprahyoid and infrahyoid muscle activity during swallowing in healthy adults.

### 1. Participants

After obtaining the approval of the Institutional Review Board of Seoul National University Hospital [1406-018-585], we conducted a single-center, pros-pective, cross-sectional study on 10 consenting healthy participants (9 men and 1 woman) aged 29.50±1.18 (mean±standard deviation [SD]) years. None of the participants had a history of major medical problems, such as dysphagia or neurological disorders.

### 2. Electromyographic techniques

The sEMG study was performed on two muscle groups: (1) suprahyoid muscles and (2) infrahyoid muscles, which are both covered by the platysma. Electrodes were positioned as follows.(Fig. 1) First, electrodes were placed 1 cm away from the midline on both sides of the skin beneath the mandibular body to record suprahyoid muscle group activity. Second, electrodes were placed 1 cm away from the midline on both sides of the thyroid cartilage to record infrahyoid muscle group activity. Third, a single electrode was affixed to the chin as the ground electrode.

Figure 1. Experimental setup. (A) Circles show the location of the electromyography electrodes on suprahyoid and infrahyoid muscle groups. (B) 1 cm away from the midline on both sides of the skin beneath the body of mandible for the suprahyoid muscle group, and thyroid cartilage for the infrahyoid muscle group.

The 4-channel sEMG was recorded using an EMG device (NicoletEDX; CareFusion, Middleton WI, USA) and software (Synergy v.20.0; CareFusion). We performed sEMG recordings using an EMG device with a bandpass filter frequency of 50-300 Hz, notch filter frequency of 60 Hz, and a sampling rate of 48 kHz.

The raw EMG signal was exported into text files, rectified using the root mean square method, and smoothed with a moving average of 40 ms time constant using in-house MATLAB (version 7.4, MathWorks Inc., Massachusetts, USA) scripts. Traces showing normal swallowing as recorded using sEMG, such as those shown in Fig. 2, were displayed on the computer screen. Cursors were automatically placed on the EMG activity of each muscle at onset and offset points, defined as points where rectified EMG signals exceeded a threshold. The threshold was calculated using the formula presented in a previous study8:

Figure 2. Traces of a normal swallow as recorded on surface electromyography (EMG). (A) Rectified, filtered, (B) and smoothed EMG wave form.
$Threshold=μ+2σ$

where μ and σ are the mean and SD, respectively, of the rectified EMG activity during a period of muscle inactivity. To confirm the onset and offset points, automatically placed cursors were adjusted manually by the investigators. Thereafter, the software calculated the duration, peak amplitude, latency of the peak amplitude, and area under the curve of the rectified EMG signal. To evaluate the intra-rater and inter-rater reliabilities of the measurement, three experienced electromyographers, who were blinded to participant information, independently measured these parameters 3 times for each swallow.

### 3. Procedures

Participants were seated comfortably on a chair, and four volumes of liquid were swallowed five times each: voluntary single swallow of saliva (“dry” swallow), then 2 ml, 5 ml, and 20 ml of water. There was an interval of 30 s between each trial of the same volume, and 3 min before increasing the volume.

The participants were presented with four different volumes of liquid and asked to hold them until the cue light turned on and to swallow as soon as they saw the cue light on. The sEMG, which was syn-chronized with the cue light, was recorded for five swallows of each of the four different volumes, totaling to 20 swallows. The participants were blinded to the EMG signals throughout the experiments, and no auditory feedback (EMG sound) was heard during the recording.

### 4. Synchronization

We manufactured a device that was used to synchronize the swallowing start signal for participants and that on the EMG device.(Fig. 3) When the instructor pressed the ‘ON’ button of the device, the LED (Light-Emitting Diode) lamp lighted up. Participants were instructed to hold water in their mouths until they see the LED lamp light up, upon which signal they were to swallow. Simultaneously, the starting signal was recorded on the EMG device. The intensity of the starting signal was set to a value greater than 100 μV for a better signal display.

Figure 3. Experimental setup. (A) The participants start to swallow after the LED light bulb turns on. (B) The electromyography starting signal appears on the screen at the same time.

### 5. Statistics

Statistical analysis was performed using the Statistical Package for Social Sciences version 19.0 (SPSS 19.0; SPSS Inc., Chicago, IL, USA). To verify the normality of the data, basic descriptive statistics (median, minimum and maximum value) were calculated for every variable. The Mann Whitney U-test was used for paired data to analyze the difference between suprahyoid and infrahyoid muscle activity. The difference in parameters corresponding to different swallow volumes was evaluated using Kruskall Wallis test. Intra-rater and inter-rater reliabilities were assessed using the intraclass correlation coefficient (ICC) for all dependent variables. The coefficient of variation, which is defined as the SD divided by the mean, was used to quantify the intrasubject variability. The level of significance for all analyses was set at P<0.05.

### RESULTS

The basic descriptive statistics of parameters obtained by sEMG during various tests are shown in Tables 1and 2. The reliability was acceptable (ICC>0.80) for all dependent variables.(Table 3)

The parameters of suprahyoid surface electromyography..

Onset (s)Offset (s)Duration (s)Latency of the peak
amplitude (s)
Maximal amplitude (μV)Area
Swallow of saliva
Median0.441.461.000.4996.6631.67
(Min.–Max.)(0.07–1.25)(0.75–2.67)(0.63–1.89)(0.09–0.96)(47.09–321.63)(5.74–106.31)
CV (%)38.0%14.7%12.9%30.1%17.5%30.3%
Small amount of fluid (2 ml)
Median0.311.260.850.3696.4526.49
(Min.–Max.)(0.07–1.09)(0.80–2.38)(0.58–1.84)(0.05–0.85)(39.05–320.17)(6.76–84.39)
CV (%)37.4%12.9%12.0%25.1%19.0%25.9%
Large amount of fluid (5 ml)
Median0.301.230.870.35699.0828.20
(Min.–Max.)(0.05–1.44)(0.68–2.28)(0.59–1.74)(0.07–3.90)(36.65–352.35)(4.74–74.84)
CV (%)34.0%14.3%12.5%56.5%19.0%26.1%
Cup drinking (20 ml)
Median0.331.380.990.45102.3536.60
(Min.–Max.)(0.02–0.88)(0.78–2.28)(0.65–1.75)(0.07–2.96)(44.85–309.72)(12.62–76.14)
CV (%)35.5%12.8%13.7%36.7%17.7%26.7%
Total
Median0.331.320.950.4098.7630.50
(Min.–Max.)(0.02–1.44)(0.68–2.67)(0.58–1.89)(0.05–3.90)(36.65–352.35)(4.74–106.31)
CV (%)45.7%17.5%15.8%57.6%24.9%36.9%

CV: coefficient of variation..

The parameters of infrahyoid surface EMG during various tests..

Onset (s)Offset (s)Duration (s)Latency of the peak
amplitude (s)
Maximal amplitude (μV)Area
Swallow of saliva
Median0.491.651.110.60103.2635.56
(Min.–Max.)(0.05–1.28)(0.85–2.75)(0.67–2.10)(0.17–2.78)(32.89–261.73)(6.70–151.90)
CV (%)33.6%15.2%14.7%33.2%19.4%27.5%
Small amount of fluid (2 ml)
Median0.411.410.990.46107.2236.47
(Min.–Max.)(0.04–1.09)(0.76–2.37)(0.61–1.98)(0.13–0.91)(34.24–184.23)(8.80–70.23)
CV (%)30.7%13.5%14.6%24.2%15.2%25.2%
Large amount of fluid (5 ml)
Median0.411.470.980.43107.8637.05
(Min.–Max.)(0.17–1.41)(0.98–2.41)(0.65–1.62)(0.12–2.89)(33.11–186.89)(16.39–104.45)
CV (%)25.4%12.5%14.0%36.0%18.4%25.3%
Cup drinking (20 ml)
Median0.431.541.080.48117.4944.11
(Min.–Max.)(0.14–1.22)(0.96–2.49)(0.71–1.87)(0.12–3.37)(36.07–187.98)(23.34–97.98)
CV (%)25.7%12.3%12.5%34.1%14.2%20.6%
Total
Median0.441.511.050.48107.8038.00
(Min.–Max.)(0.04–1.41)(0.76–2.75)(0.61–2.10)(0.12–3.37)(32.89–261.73)(6.70–151.90)
CV (%)33.2%16.5%17.2%44.0%20.9%30.9%

CV: coefficient of variation..

Intra- and inter-rater reliability using intraclass correlation coefficient for all dependent variables..

Intra-rater reliabilityInter-rater reliability

ICC95% CIICC95% CI

LowerUpperLowerUpper
Right suprahyoid muscles
Onset (s)0.9940.9890.9960.9770.9610.987
Offset (s)0.9850.9750.9920.9780.9630.988
Duration (s)0.9660.9420.9810.9070.8420.948
Latency to maximum (s)0.9980.9960.9990.9930.9880.996
Area0.9870.9790.9930.9600.9330.978
Left suprahyoid muscles
Onset (s)0.9880.9800.9930.9710.9500.983
Offset (s)0.9850.9750.9920.9800.9660.989
Duration (s)0.9510.9180.9730.8770.7920.931
Latency to maximum (s)0.9980.9970.9990.9940.9900.997
Area0.9910.9850.9950.9620.9350.978
Right infrahyoid muscles
Onset (s)0.9780.9630.9880.9700.9490.983
Offset (s)0.9820.9700.9900.9720.9520.984
Duration (s)0.9370.8930.9640.9060.8410.947
Latency to maximum (s)0.9970.9940.9980.9900.9830.994
Area0.9900.9830.9940.9620.9360.979
Left infrahyoid muscles
Onset (s)0.9770.9620.9870.9680.9460.982
Offset (s)0.9690.9480.9830.9690.9480.983
Duration (s)0.8900.8140.9380.8430.7350.912
Latency to maximum (s)0.9990.9980.9990.9970.9940.998
Area0.9790.9640.9880.9780.9630.988

ICC: intraclass correlation coefficient, CI: confidence interval..

### 1. Comparison between suprahyoid and infrahyoid muscle activities

The results indicated that the onset and offset latencies of suprahyoid muscles were shorter than those of infrahyoid muscles (P<0.001); moreover, the suprahyoid muscle group had a significantly shorter duration of dry and water swallows than the infrahyoid muscle group. When comparing the results of dry and water swallows by total fluids (2 ml, 5 ml, 50 ml combined) as indicated in Tables 1and 2by “total”, there was no significant difference of peak amplitude between the two muscle. The mean of the area under the curve of the rectified EMG signal for all volumes of fluid swallowed was significantly larger in infrahyoid than in suprahyoid muscles.

### 2. Comparison between the different swallowed fluid volumes

In a subgroup analysis wherein saliva swallow was excluded, the peak amplitude of swallow showed a clear linear tendency to increase with the volume of swallowed liquid in the suprahyoid (P=0.309) and significant difference in the infrahyoid muscle (P=0.013) groups. The area under the curve of the EMG signal increased proportionately with the amount of fluid swallowed in both muscle groups (P=0.002, suprahyoid; P<0.001, infrahyoid) and was significantly larger in the 20 ml volume swallow of water than in the other volume swallows.

### 3. Intrasubject variability

The intrasubject variability of the offset latency, duration, and maximal amplitude was lower (less than 30% of the coefficient of variation) than that of the other parameters among the different swallow trials in the same participants.(Table 2)

### DISCUSSION

Dysphagia is associated with prolonged hospitali-zation and a higher risk of mortality in many patients. However, it is difficult to determine the exact prevalence of dysphagia because of its diverse etiologies and the complexity of the evaluation of swallowing. For this reason, the prevalence of dysphagia fluctuates in different published studies. Some studies revealed that the prevalence varies from 0.35% to 55% in the acute care unit2,9,10; moreover, the figures are more pronounced in the nursing home setting with prevalence rates ranging from 55% to 68%11,12. Notably, dysphagia is often present in individuals with neurological disorders or other general medical problems. Some investigators conducted a study in four European countries and found a high proportion of patients with comorbidities of up to 81%13. Hence, a simple screening tool is required to perform a rapid assessment of patients with dysphagia. However, the complex mechanism of swallowing renders the evaluation of dysphagia difficult. Many different diagnostic techniques have been proposed such as computerized axial tomo-graphy, magnetic resonance imaging, barium esopha-gogram, air contrast esophagogram, manometry, fiberoptic endoscopic evaluation of swallowing, bolus scintigraphy, ultrasonography, and VFSS3,14. In current clinical practice, the evaluation of dysphagia is mainly based on VFSS, and it is considered the gold standard in dysphagia assessment3. However, VFSS cannot be performed daily as a simple screening tool due to its use of radiation. Moreover, VFSS cannot evaluate individual muscle activation because it only provides motion recordings.

Swallowing functions have been widely studied using sEMG3,8,15-19. With the recent emphasis on the use of noninvasive techniques for patient evaluation, sEMG emerges as a simple and easy-to-operate, radiation-free, inexpensive, and time-saving screening tool that can provide both qualitative and quan-titative data. Despite these advantages, limited information regarding normative data and reliability is available to permit the use of sEMG in clinical practice. Some studies have investigated the normative data and reliability of EMG activity during normal swallowing8,15-18,20,21. The range of normal values presented varies widely because of the large variation in technical factors such as electrode position, examination protocol, or result interpreta-tions among physicians. It is suggested that the procedures and value of sEMG studies may be further improved by international standardization.

This study was undertaken to explore normative data and reliability for several parameters. We sought to investigate possible significant differences in the parameters of EMG activity in the infrahyoid and suprahyoid muscle groups during swallowing. Moreover, we investigated possible significant differences in parameters of EMG activity during dry, normal (2 ml and 5 ml), and excessive (20 ml) swallow.

We found that the onset and offset latency of the suprahyoid muscles shorter than those of the infrahyoid muscles. Furthermore, the suprahyoid muscle group had a shorter swallowing duration compared with the infrahyoid muscle group. Some authors found that there were no significant differences in offset latency and swallowing duration between the two muscle groups15; nevertheless, the difference in the definition of the parameters renders direct comparison difficult.

Comparing muscle activity in the different volumes of fluid swallowed, Vaiman et al.15,16found that the duration of swallow increased with volume of swallowed fluid in the suprahyoid muscle group; moreover, there was a significant difference in the duration of swallow between the normal and excessive swallow of water. In our study, the duration of saliva swallow was slightly longer than that of water swallow. This difference is thought to be due to differences in the study procedures. The authors of the previous study15performed normal swallow of tap water first followed by dry swallow, whereas dry swallow proceeded water swallow in our study. Differences in the order of swallow resulted in differences in mouth dryness, and an increased muscular effort was required to initiate the dry swallow in our study. This muscular effort could similarly affect the peak EMG amplitude of swallowing. These findings corroborated with those of Hughes et al.22, who showed that the duration of saliva swallow is longer than that of water swallow for all individuals.

In the subgroup analysis wherein saliva swallow was excluded, we found that the peak amplitude increased with volume in both the suprahyoid and infrahyoid muscle groups. This contradicted the finding of Vaiman et al.15,16who found that the range of EMG activity during 20 ml swallow (“stress” test) was significantly lower than that during normal swallow. We may assume that the adaptation for larger volume accommodation resulted in an increase in muscular effort in our study instead of duration prolongation, as shown in previous studies15,16. However, for participants aged at least 61 years, the results of Vaiman et al.16corresponded to those of our study, which showed that the range of submental muscle EMG activity increased with the volume of swallow.

The results of our study showed an excellent agreement for intra-rater and inter-rater reliability. In general, the ICCs for suprahyoid and infrahyoid muscles in this study were in line with the results of previous study23that have shown that the intra- and inter-rater reliability using sEMG for monitoring submental muscle activity during swallowing was excellent for all variables and ranged from .98 to .99 and from .88 to .99. In our study, the recording and processing of the sEMG data using MATLAB was carried out by experienced electromyographers in the same settings of our laboratory. Therefore, the results cannot be generalized to other settings. More sEMG studies will be needed in order to assess reliability in different settings.

In our study, intra-individual variability evaluated using the coefficient of variation showed that offset latency, duration, and maximal amplitude of EMG activity were more reliable than the other para-meters. Huckabee et al.19evaluated the variability in sEMG recording of submental muscle activity during swallowing in healthy participants and found no significant differences across swallow trials within a single session of the same condition on the submental sEMG peak amplitude. Another study22on the relationship between dysphagia and salivary gland dysfunction revealed that the duration of the third swallow trial was longer than that of the first trial in a series of three saliva swallows. Intra-individual variability could be affected by diverse biological factors such as muscle fatigue or the amount of salivary secretion (which may vary according to the volume of liquid swallowed), time interval between each swallows, the number of trials, and sequence of the food presentation to the participants. Further research should be performed to evaluate the effect of these factors on the variability.

Our pilot study was conducted to investigate normative data and the reliability of suprahyoid and infrahyoid EMG values during swallowing in a population of apparently healthy people. The use of sEMG for the initial evaluation of swallowing is noninvasive, simple, and reliable. Moreover, sEMG is radiation-free, inexpensive, and time-saving, and it can be used with other evaluation tool such as VFSS simultaneously. We performed this present study with same protocol (voluntary single swallow of saliva, 2 ml, 5 ml, and 20 ml of water) used in VFSS evaluation of our laboratory. As a preliminary study of further research about the relationship between kinematic and electromyographic analysis, we could find that sEMG recordings is reliable tool for evaluating swallow and can be used with VFSS for investigating kinematic and electrophysiologic data.

Nonetheless, our study had the following limitations. First, sEMG could have measured the sum of the activities of all muscles under the skin in the facial area. The muscles evaluated in this study are covered by platysma, and the relaxation of this muscle is essential for reducing the variability. In the present study, only a small number of healthy participants were tested. Further investigations with larger sample sizes are necessary. Furthermore, studies correlating sEMG with videofluoroscopy will provide kinematic information and clarify the relationship between muscle activity and swallowing movement.

In summary, intra-individual swallow-to-swallow variability should be considered when using suprahyoid and infrahyoid sEMG activities as an outcome measure in research. More cautious planning seems warranted, such as taking the average of multiple (>4 times) swallows for each diet, controlling any visual or auditory feedback throughout the experiments, and the consideration of food volume. Among EMG parameters, the offset latency, swallowing duration, and maximal amplitude of the rectified signal seem to be the least variable parameters in terms of intra-individual variability for multiple trials. Large- scale clinical studies are required to establish the reference range of sEMG parameters for these muscles that are related to swallowing.

### References

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### Article

#### Original Article

J Korean Dysphagia Soc 2021; 11(2): 128-136

Published online July 30, 2021 https://doi.org/10.34160/jkds.2021.11.2.007

## Reliability of Suprahyoid and Infrahyoid Electromyographic Measurements during Swallowing in Healthy Subjects

Myung Woo Park, M.D.1, Dongheon Lee, Ph.D.2, Han Gil Seo, M.D., Ph.D.1,7, Tai Ryoon Han, M.D., Ph.D.1, Jung Chan Lee, Ph.D.3,4,5, Hee Chan Kim, Ph.D.4,5,6, Byung-Mo Oh, M.D., Ph.D.1,7,8,9,10

1Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, 2Biomedical Research Institute, Seoul National University Hospital, Seoul, 3Department of Biomedical Engineering, Seoul National University Hospital, Seoul, 4Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 5Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, 6Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 7Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, 8Institute on Aging, Seoul National University, Seoul, 9National Traffic Injury Rehabilitation Hospital, Yangpyeong, 10Neuroscience Research Institute, Seoul National University College of Medicine, Seoul, Korea

Correspondence to:Byung-Mo Oh, Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: +82-2-2072-2619, Fax: +82-2-6072-5244
E-mail: moya1@snu.ac.kr

Received: March 8, 2021; Revised: March 9, 2021; Accepted: June 6, 2021

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

### Abstract

Objective: To evaluate the reliability of suprahyoid and infrahyoid electromyography (EMG) measurement during swallowing.
Methods: In all, 10 healthy volunteers were evaluated for the following surface EMG (sEMG) parameters in the suprahyoid and infrahyoid muscles during swallowing: onset latency, offset latency, duration, peak latency, maximal amplitude during swallowing, and the area under curve (AUC) of the rectified EMG signal. The sEMG was recorded while the participants swallowed five times each of the four fluid volumes (saliva, 2 ml, 5 ml, and 20 ml of water), totaling to 20 swallows. Moreover, the intra-participant variability per parameter was evaluated using the coefficient of variation (CV).
Results: Suprahyoid muscles were activated 0.095 s (95% CI, 0.062-0.128) earlier than the infrahyoid muscles. Maximal amplitudes during the 20 ml swallow were 17.484 (−1.543-36.512) and 13.490 (1.254-25.727) μV higher than values obtained during the 2 ml swallow in the suprahyoid and infrahyoid muscles, respectively. Furthermore, the AUC of the rectified EMG signal increased with the volume of swallow in both muscle groups (P=0.003, suprahyoid; P<0.002, infrahyoid). The intra-individual variabilities of offset latency, duration, and maximal amplitude were relatively low (<30% CV) in both muscle groups with respect to other parameters. The assessment of each parameter using EMG was highly reliable, with an intraclass correlation coefficient of ＞0.8.
Conclusion: Among the variable sEMG parameters assessed, the offset latency, duration, and maximal amplitude were the least variable. Although reliability on the rater side showed good results, the swallow-to-swallow variability of the parameters need to be considered in swallowing studies using sEMG

Keywords: Electromyography, Deglutition disorders, Reliability of results

### INTRODUCTION

Swallowing is a complex process involving a coordinated activation of many muscles, including the oral, pharyngeal, and laryngeal muscles, at different levels of the central nervous system from the cerebral cortex to the medulla oblongata1. The prevalence of dysphagia is very high in elderly patients, and it affects more than 30% of patients with stroke, 60-80% of patients with neurodegenerative disease, and more than 51% of institutionalized elderly patients2. Physicians in various fields frequently encounter patients with these disorders; therefore, a simple and noninvasive screening tool is required for such patients. Currently, videofluoroscopic swallowing study (VFSS) is the gold standard for evaluating patients with dysphagia; however, it uses radiation, requires radiologic equipment and personnel, and is expensive3. With the increasing need for a simple and rapid screening tool for dysphagia, surface electro-myography (sEMG) could be considered as a valuable method for evaluating dysphagia because of its noninvasive, radiation-free, inexpensive, and time- saving features. Various studies have used sEMG to evaluate the physiology of swallowing and the pathophysiological mechanisms involved in patients with dysphagia4-7. Despite the advantages of sEMG, there is a paucity of studies with normative data for normal swallowing and the reliability of sEMG for swallowing function evaluation.

This is a preliminary study evaluating the reliability of sEMG and investigating normative data for suprahyoid and infrahyoid muscle activity during swallowing in healthy adults.

### 1. Participants

After obtaining the approval of the Institutional Review Board of Seoul National University Hospital [1406-018-585], we conducted a single-center, pros-pective, cross-sectional study on 10 consenting healthy participants (9 men and 1 woman) aged 29.50±1.18 (mean±standard deviation [SD]) years. None of the participants had a history of major medical problems, such as dysphagia or neurological disorders.

### 2. Electromyographic techniques

The sEMG study was performed on two muscle groups: (1) suprahyoid muscles and (2) infrahyoid muscles, which are both covered by the platysma. Electrodes were positioned as follows.(Fig. 1) First, electrodes were placed 1 cm away from the midline on both sides of the skin beneath the mandibular body to record suprahyoid muscle group activity. Second, electrodes were placed 1 cm away from the midline on both sides of the thyroid cartilage to record infrahyoid muscle group activity. Third, a single electrode was affixed to the chin as the ground electrode.

Figure 1. Experimental setup. (A) Circles show the location of the electromyography electrodes on suprahyoid and infrahyoid muscle groups. (B) 1 cm away from the midline on both sides of the skin beneath the body of mandible for the suprahyoid muscle group, and thyroid cartilage for the infrahyoid muscle group.

The 4-channel sEMG was recorded using an EMG device (NicoletEDX; CareFusion, Middleton WI, USA) and software (Synergy v.20.0; CareFusion). We performed sEMG recordings using an EMG device with a bandpass filter frequency of 50-300 Hz, notch filter frequency of 60 Hz, and a sampling rate of 48 kHz.

The raw EMG signal was exported into text files, rectified using the root mean square method, and smoothed with a moving average of 40 ms time constant using in-house MATLAB (version 7.4, MathWorks Inc., Massachusetts, USA) scripts. Traces showing normal swallowing as recorded using sEMG, such as those shown in Fig. 2, were displayed on the computer screen. Cursors were automatically placed on the EMG activity of each muscle at onset and offset points, defined as points where rectified EMG signals exceeded a threshold. The threshold was calculated using the formula presented in a previous study8:

Figure 2. Traces of a normal swallow as recorded on surface electromyography (EMG). (A) Rectified, filtered, (B) and smoothed EMG wave form.
$Threshold=μ+2σ$

where μ and σ are the mean and SD, respectively, of the rectified EMG activity during a period of muscle inactivity. To confirm the onset and offset points, automatically placed cursors were adjusted manually by the investigators. Thereafter, the software calculated the duration, peak amplitude, latency of the peak amplitude, and area under the curve of the rectified EMG signal. To evaluate the intra-rater and inter-rater reliabilities of the measurement, three experienced electromyographers, who were blinded to participant information, independently measured these parameters 3 times for each swallow.

### 3. Procedures

Participants were seated comfortably on a chair, and four volumes of liquid were swallowed five times each: voluntary single swallow of saliva (“dry” swallow), then 2 ml, 5 ml, and 20 ml of water. There was an interval of 30 s between each trial of the same volume, and 3 min before increasing the volume.

The participants were presented with four different volumes of liquid and asked to hold them until the cue light turned on and to swallow as soon as they saw the cue light on. The sEMG, which was syn-chronized with the cue light, was recorded for five swallows of each of the four different volumes, totaling to 20 swallows. The participants were blinded to the EMG signals throughout the experiments, and no auditory feedback (EMG sound) was heard during the recording.

### 4. Synchronization

We manufactured a device that was used to synchronize the swallowing start signal for participants and that on the EMG device.(Fig. 3) When the instructor pressed the ‘ON’ button of the device, the LED (Light-Emitting Diode) lamp lighted up. Participants were instructed to hold water in their mouths until they see the LED lamp light up, upon which signal they were to swallow. Simultaneously, the starting signal was recorded on the EMG device. The intensity of the starting signal was set to a value greater than 100 μV for a better signal display.

Figure 3. Experimental setup. (A) The participants start to swallow after the LED light bulb turns on. (B) The electromyography starting signal appears on the screen at the same time.

### 5. Statistics

Statistical analysis was performed using the Statistical Package for Social Sciences version 19.0 (SPSS 19.0; SPSS Inc., Chicago, IL, USA). To verify the normality of the data, basic descriptive statistics (median, minimum and maximum value) were calculated for every variable. The Mann Whitney U-test was used for paired data to analyze the difference between suprahyoid and infrahyoid muscle activity. The difference in parameters corresponding to different swallow volumes was evaluated using Kruskall Wallis test. Intra-rater and inter-rater reliabilities were assessed using the intraclass correlation coefficient (ICC) for all dependent variables. The coefficient of variation, which is defined as the SD divided by the mean, was used to quantify the intrasubject variability. The level of significance for all analyses was set at P<0.05.

### RESULTS

The basic descriptive statistics of parameters obtained by sEMG during various tests are shown in Tables 1and 2. The reliability was acceptable (ICC>0.80) for all dependent variables.(Table 3)

The parameters of suprahyoid surface electromyography..

Onset (s)Offset (s)Duration (s)Latency of the peak
amplitude (s)
Maximal amplitude (μV)Area
Swallow of saliva
Median0.441.461.000.4996.6631.67
(Min.–Max.)(0.07–1.25)(0.75–2.67)(0.63–1.89)(0.09–0.96)(47.09–321.63)(5.74–106.31)
CV (%)38.0%14.7%12.9%30.1%17.5%30.3%
Small amount of fluid (2 ml)
Median0.311.260.850.3696.4526.49
(Min.–Max.)(0.07–1.09)(0.80–2.38)(0.58–1.84)(0.05–0.85)(39.05–320.17)(6.76–84.39)
CV (%)37.4%12.9%12.0%25.1%19.0%25.9%
Large amount of fluid (5 ml)
Median0.301.230.870.35699.0828.20
(Min.–Max.)(0.05–1.44)(0.68–2.28)(0.59–1.74)(0.07–3.90)(36.65–352.35)(4.74–74.84)
CV (%)34.0%14.3%12.5%56.5%19.0%26.1%
Cup drinking (20 ml)
Median0.331.380.990.45102.3536.60
(Min.–Max.)(0.02–0.88)(0.78–2.28)(0.65–1.75)(0.07–2.96)(44.85–309.72)(12.62–76.14)
CV (%)35.5%12.8%13.7%36.7%17.7%26.7%
Total
Median0.331.320.950.4098.7630.50
(Min.–Max.)(0.02–1.44)(0.68–2.67)(0.58–1.89)(0.05–3.90)(36.65–352.35)(4.74–106.31)
CV (%)45.7%17.5%15.8%57.6%24.9%36.9%

CV: coefficient of variation..

The parameters of infrahyoid surface EMG during various tests..

Onset (s)Offset (s)Duration (s)Latency of the peak
amplitude (s)
Maximal amplitude (μV)Area
Swallow of saliva
Median0.491.651.110.60103.2635.56
(Min.–Max.)(0.05–1.28)(0.85–2.75)(0.67–2.10)(0.17–2.78)(32.89–261.73)(6.70–151.90)
CV (%)33.6%15.2%14.7%33.2%19.4%27.5%
Small amount of fluid (2 ml)
Median0.411.410.990.46107.2236.47
(Min.–Max.)(0.04–1.09)(0.76–2.37)(0.61–1.98)(0.13–0.91)(34.24–184.23)(8.80–70.23)
CV (%)30.7%13.5%14.6%24.2%15.2%25.2%
Large amount of fluid (5 ml)
Median0.411.470.980.43107.8637.05
(Min.–Max.)(0.17–1.41)(0.98–2.41)(0.65–1.62)(0.12–2.89)(33.11–186.89)(16.39–104.45)
CV (%)25.4%12.5%14.0%36.0%18.4%25.3%
Cup drinking (20 ml)
Median0.431.541.080.48117.4944.11
(Min.–Max.)(0.14–1.22)(0.96–2.49)(0.71–1.87)(0.12–3.37)(36.07–187.98)(23.34–97.98)
CV (%)25.7%12.3%12.5%34.1%14.2%20.6%
Total
Median0.441.511.050.48107.8038.00
(Min.–Max.)(0.04–1.41)(0.76–2.75)(0.61–2.10)(0.12–3.37)(32.89–261.73)(6.70–151.90)
CV (%)33.2%16.5%17.2%44.0%20.9%30.9%

CV: coefficient of variation..

Intra- and inter-rater reliability using intraclass correlation coefficient for all dependent variables..

Intra-rater reliabilityInter-rater reliability

ICC95% CIICC95% CI

LowerUpperLowerUpper
Right suprahyoid muscles
Onset (s)0.9940.9890.9960.9770.9610.987
Offset (s)0.9850.9750.9920.9780.9630.988
Duration (s)0.9660.9420.9810.9070.8420.948
Latency to maximum (s)0.9980.9960.9990.9930.9880.996
Area0.9870.9790.9930.9600.9330.978
Left suprahyoid muscles
Onset (s)0.9880.9800.9930.9710.9500.983
Offset (s)0.9850.9750.9920.9800.9660.989
Duration (s)0.9510.9180.9730.8770.7920.931
Latency to maximum (s)0.9980.9970.9990.9940.9900.997
Area0.9910.9850.9950.9620.9350.978
Right infrahyoid muscles
Onset (s)0.9780.9630.9880.9700.9490.983
Offset (s)0.9820.9700.9900.9720.9520.984
Duration (s)0.9370.8930.9640.9060.8410.947
Latency to maximum (s)0.9970.9940.9980.9900.9830.994
Area0.9900.9830.9940.9620.9360.979
Left infrahyoid muscles
Onset (s)0.9770.9620.9870.9680.9460.982
Offset (s)0.9690.9480.9830.9690.9480.983
Duration (s)0.8900.8140.9380.8430.7350.912
Latency to maximum (s)0.9990.9980.9990.9970.9940.998
Area0.9790.9640.9880.9780.9630.988

ICC: intraclass correlation coefficient, CI: confidence interval..

### 1. Comparison between suprahyoid and infrahyoid muscle activities

The results indicated that the onset and offset latencies of suprahyoid muscles were shorter than those of infrahyoid muscles (P<0.001); moreover, the suprahyoid muscle group had a significantly shorter duration of dry and water swallows than the infrahyoid muscle group. When comparing the results of dry and water swallows by total fluids (2 ml, 5 ml, 50 ml combined) as indicated in Tables 1and 2by “total”, there was no significant difference of peak amplitude between the two muscle. The mean of the area under the curve of the rectified EMG signal for all volumes of fluid swallowed was significantly larger in infrahyoid than in suprahyoid muscles.

### 2. Comparison between the different swallowed fluid volumes

In a subgroup analysis wherein saliva swallow was excluded, the peak amplitude of swallow showed a clear linear tendency to increase with the volume of swallowed liquid in the suprahyoid (P=0.309) and significant difference in the infrahyoid muscle (P=0.013) groups. The area under the curve of the EMG signal increased proportionately with the amount of fluid swallowed in both muscle groups (P=0.002, suprahyoid; P<0.001, infrahyoid) and was significantly larger in the 20 ml volume swallow of water than in the other volume swallows.

### 3. Intrasubject variability

The intrasubject variability of the offset latency, duration, and maximal amplitude was lower (less than 30% of the coefficient of variation) than that of the other parameters among the different swallow trials in the same participants.(Table 2)

### DISCUSSION

Dysphagia is associated with prolonged hospitali-zation and a higher risk of mortality in many patients. However, it is difficult to determine the exact prevalence of dysphagia because of its diverse etiologies and the complexity of the evaluation of swallowing. For this reason, the prevalence of dysphagia fluctuates in different published studies. Some studies revealed that the prevalence varies from 0.35% to 55% in the acute care unit2,9,10; moreover, the figures are more pronounced in the nursing home setting with prevalence rates ranging from 55% to 68%11,12. Notably, dysphagia is often present in individuals with neurological disorders or other general medical problems. Some investigators conducted a study in four European countries and found a high proportion of patients with comorbidities of up to 81%13. Hence, a simple screening tool is required to perform a rapid assessment of patients with dysphagia. However, the complex mechanism of swallowing renders the evaluation of dysphagia difficult. Many different diagnostic techniques have been proposed such as computerized axial tomo-graphy, magnetic resonance imaging, barium esopha-gogram, air contrast esophagogram, manometry, fiberoptic endoscopic evaluation of swallowing, bolus scintigraphy, ultrasonography, and VFSS3,14. In current clinical practice, the evaluation of dysphagia is mainly based on VFSS, and it is considered the gold standard in dysphagia assessment3. However, VFSS cannot be performed daily as a simple screening tool due to its use of radiation. Moreover, VFSS cannot evaluate individual muscle activation because it only provides motion recordings.

Swallowing functions have been widely studied using sEMG3,8,15-19. With the recent emphasis on the use of noninvasive techniques for patient evaluation, sEMG emerges as a simple and easy-to-operate, radiation-free, inexpensive, and time-saving screening tool that can provide both qualitative and quan-titative data. Despite these advantages, limited information regarding normative data and reliability is available to permit the use of sEMG in clinical practice. Some studies have investigated the normative data and reliability of EMG activity during normal swallowing8,15-18,20,21. The range of normal values presented varies widely because of the large variation in technical factors such as electrode position, examination protocol, or result interpreta-tions among physicians. It is suggested that the procedures and value of sEMG studies may be further improved by international standardization.

This study was undertaken to explore normative data and reliability for several parameters. We sought to investigate possible significant differences in the parameters of EMG activity in the infrahyoid and suprahyoid muscle groups during swallowing. Moreover, we investigated possible significant differences in parameters of EMG activity during dry, normal (2 ml and 5 ml), and excessive (20 ml) swallow.

We found that the onset and offset latency of the suprahyoid muscles shorter than those of the infrahyoid muscles. Furthermore, the suprahyoid muscle group had a shorter swallowing duration compared with the infrahyoid muscle group. Some authors found that there were no significant differences in offset latency and swallowing duration between the two muscle groups15; nevertheless, the difference in the definition of the parameters renders direct comparison difficult.

Comparing muscle activity in the different volumes of fluid swallowed, Vaiman et al.15,16found that the duration of swallow increased with volume of swallowed fluid in the suprahyoid muscle group; moreover, there was a significant difference in the duration of swallow between the normal and excessive swallow of water. In our study, the duration of saliva swallow was slightly longer than that of water swallow. This difference is thought to be due to differences in the study procedures. The authors of the previous study15performed normal swallow of tap water first followed by dry swallow, whereas dry swallow proceeded water swallow in our study. Differences in the order of swallow resulted in differences in mouth dryness, and an increased muscular effort was required to initiate the dry swallow in our study. This muscular effort could similarly affect the peak EMG amplitude of swallowing. These findings corroborated with those of Hughes et al.22, who showed that the duration of saliva swallow is longer than that of water swallow for all individuals.

In the subgroup analysis wherein saliva swallow was excluded, we found that the peak amplitude increased with volume in both the suprahyoid and infrahyoid muscle groups. This contradicted the finding of Vaiman et al.15,16who found that the range of EMG activity during 20 ml swallow (“stress” test) was significantly lower than that during normal swallow. We may assume that the adaptation for larger volume accommodation resulted in an increase in muscular effort in our study instead of duration prolongation, as shown in previous studies15,16. However, for participants aged at least 61 years, the results of Vaiman et al.16corresponded to those of our study, which showed that the range of submental muscle EMG activity increased with the volume of swallow.

The results of our study showed an excellent agreement for intra-rater and inter-rater reliability. In general, the ICCs for suprahyoid and infrahyoid muscles in this study were in line with the results of previous study23that have shown that the intra- and inter-rater reliability using sEMG for monitoring submental muscle activity during swallowing was excellent for all variables and ranged from .98 to .99 and from .88 to .99. In our study, the recording and processing of the sEMG data using MATLAB was carried out by experienced electromyographers in the same settings of our laboratory. Therefore, the results cannot be generalized to other settings. More sEMG studies will be needed in order to assess reliability in different settings.

In our study, intra-individual variability evaluated using the coefficient of variation showed that offset latency, duration, and maximal amplitude of EMG activity were more reliable than the other para-meters. Huckabee et al.19evaluated the variability in sEMG recording of submental muscle activity during swallowing in healthy participants and found no significant differences across swallow trials within a single session of the same condition on the submental sEMG peak amplitude. Another study22on the relationship between dysphagia and salivary gland dysfunction revealed that the duration of the third swallow trial was longer than that of the first trial in a series of three saliva swallows. Intra-individual variability could be affected by diverse biological factors such as muscle fatigue or the amount of salivary secretion (which may vary according to the volume of liquid swallowed), time interval between each swallows, the number of trials, and sequence of the food presentation to the participants. Further research should be performed to evaluate the effect of these factors on the variability.

Our pilot study was conducted to investigate normative data and the reliability of suprahyoid and infrahyoid EMG values during swallowing in a population of apparently healthy people. The use of sEMG for the initial evaluation of swallowing is noninvasive, simple, and reliable. Moreover, sEMG is radiation-free, inexpensive, and time-saving, and it can be used with other evaluation tool such as VFSS simultaneously. We performed this present study with same protocol (voluntary single swallow of saliva, 2 ml, 5 ml, and 20 ml of water) used in VFSS evaluation of our laboratory. As a preliminary study of further research about the relationship between kinematic and electromyographic analysis, we could find that sEMG recordings is reliable tool for evaluating swallow and can be used with VFSS for investigating kinematic and electrophysiologic data.

Nonetheless, our study had the following limitations. First, sEMG could have measured the sum of the activities of all muscles under the skin in the facial area. The muscles evaluated in this study are covered by platysma, and the relaxation of this muscle is essential for reducing the variability. In the present study, only a small number of healthy participants were tested. Further investigations with larger sample sizes are necessary. Furthermore, studies correlating sEMG with videofluoroscopy will provide kinematic information and clarify the relationship between muscle activity and swallowing movement.

In summary, intra-individual swallow-to-swallow variability should be considered when using suprahyoid and infrahyoid sEMG activities as an outcome measure in research. More cautious planning seems warranted, such as taking the average of multiple (>4 times) swallows for each diet, controlling any visual or auditory feedback throughout the experiments, and the consideration of food volume. Among EMG parameters, the offset latency, swallowing duration, and maximal amplitude of the rectified signal seem to be the least variable parameters in terms of intra-individual variability for multiple trials. Large- scale clinical studies are required to establish the reference range of sEMG parameters for these muscles that are related to swallowing.

### Fig 1.

Figure 1.Experimental setup. (A) Circles show the location of the electromyography electrodes on suprahyoid and infrahyoid muscle groups. (B) 1 cm away from the midline on both sides of the skin beneath the body of mandible for the suprahyoid muscle group, and thyroid cartilage for the infrahyoid muscle group.
Journal of the Korean Dysphagia Society 2021; 11: 128-136https://doi.org/10.34160/jkds.2021.11.2.007

### Fig 2.

Figure 2.Traces of a normal swallow as recorded on surface electromyography (EMG). (A) Rectified, filtered, (B) and smoothed EMG wave form.
Journal of the Korean Dysphagia Society 2021; 11: 128-136https://doi.org/10.34160/jkds.2021.11.2.007

### Fig 3.

Figure 3.Experimental setup. (A) The participants start to swallow after the LED light bulb turns on. (B) The electromyography starting signal appears on the screen at the same time.
Journal of the Korean Dysphagia Society 2021; 11: 128-136https://doi.org/10.34160/jkds.2021.11.2.007

The parameters of suprahyoid surface electromyography..

Onset (s)Offset (s)Duration (s)Latency of the peak
amplitude (s)
Maximal amplitude (μV)Area
Swallow of saliva
Median0.441.461.000.4996.6631.67
(Min.–Max.)(0.07–1.25)(0.75–2.67)(0.63–1.89)(0.09–0.96)(47.09–321.63)(5.74–106.31)
CV (%)38.0%14.7%12.9%30.1%17.5%30.3%
Small amount of fluid (2 ml)
Median0.311.260.850.3696.4526.49
(Min.–Max.)(0.07–1.09)(0.80–2.38)(0.58–1.84)(0.05–0.85)(39.05–320.17)(6.76–84.39)
CV (%)37.4%12.9%12.0%25.1%19.0%25.9%
Large amount of fluid (5 ml)
Median0.301.230.870.35699.0828.20
(Min.–Max.)(0.05–1.44)(0.68–2.28)(0.59–1.74)(0.07–3.90)(36.65–352.35)(4.74–74.84)
CV (%)34.0%14.3%12.5%56.5%19.0%26.1%
Cup drinking (20 ml)
Median0.331.380.990.45102.3536.60
(Min.–Max.)(0.02–0.88)(0.78–2.28)(0.65–1.75)(0.07–2.96)(44.85–309.72)(12.62–76.14)
CV (%)35.5%12.8%13.7%36.7%17.7%26.7%
Total
Median0.331.320.950.4098.7630.50
(Min.–Max.)(0.02–1.44)(0.68–2.67)(0.58–1.89)(0.05–3.90)(36.65–352.35)(4.74–106.31)
CV (%)45.7%17.5%15.8%57.6%24.9%36.9%

CV: coefficient of variation..

The parameters of infrahyoid surface EMG during various tests..

Onset (s)Offset (s)Duration (s)Latency of the peak
amplitude (s)
Maximal amplitude (μV)Area
Swallow of saliva
Median0.491.651.110.60103.2635.56
(Min.–Max.)(0.05–1.28)(0.85–2.75)(0.67–2.10)(0.17–2.78)(32.89–261.73)(6.70–151.90)
CV (%)33.6%15.2%14.7%33.2%19.4%27.5%
Small amount of fluid (2 ml)
Median0.411.410.990.46107.2236.47
(Min.–Max.)(0.04–1.09)(0.76–2.37)(0.61–1.98)(0.13–0.91)(34.24–184.23)(8.80–70.23)
CV (%)30.7%13.5%14.6%24.2%15.2%25.2%
Large amount of fluid (5 ml)
Median0.411.470.980.43107.8637.05
(Min.–Max.)(0.17–1.41)(0.98–2.41)(0.65–1.62)(0.12–2.89)(33.11–186.89)(16.39–104.45)
CV (%)25.4%12.5%14.0%36.0%18.4%25.3%
Cup drinking (20 ml)
Median0.431.541.080.48117.4944.11
(Min.–Max.)(0.14–1.22)(0.96–2.49)(0.71–1.87)(0.12–3.37)(36.07–187.98)(23.34–97.98)
CV (%)25.7%12.3%12.5%34.1%14.2%20.6%
Total
Median0.441.511.050.48107.8038.00
(Min.–Max.)(0.04–1.41)(0.76–2.75)(0.61–2.10)(0.12–3.37)(32.89–261.73)(6.70–151.90)
CV (%)33.2%16.5%17.2%44.0%20.9%30.9%

CV: coefficient of variation..

Intra- and inter-rater reliability using intraclass correlation coefficient for all dependent variables..

Intra-rater reliabilityInter-rater reliability

ICC95% CIICC95% CI

LowerUpperLowerUpper
Right suprahyoid muscles
Onset (s)0.9940.9890.9960.9770.9610.987
Offset (s)0.9850.9750.9920.9780.9630.988
Duration (s)0.9660.9420.9810.9070.8420.948
Latency to maximum (s)0.9980.9960.9990.9930.9880.996
Area0.9870.9790.9930.9600.9330.978
Left suprahyoid muscles
Onset (s)0.9880.9800.9930.9710.9500.983
Offset (s)0.9850.9750.9920.9800.9660.989
Duration (s)0.9510.9180.9730.8770.7920.931
Latency to maximum (s)0.9980.9970.9990.9940.9900.997
Area0.9910.9850.9950.9620.9350.978
Right infrahyoid muscles
Onset (s)0.9780.9630.9880.9700.9490.983
Offset (s)0.9820.9700.9900.9720.9520.984
Duration (s)0.9370.8930.9640.9060.8410.947
Latency to maximum (s)0.9970.9940.9980.9900.9830.994
Area0.9900.9830.9940.9620.9360.979
Left infrahyoid muscles
Onset (s)0.9770.9620.9870.9680.9460.982
Offset (s)0.9690.9480.9830.9690.9480.983
Duration (s)0.8900.8140.9380.8430.7350.912
Latency to maximum (s)0.9990.9980.9990.9970.9940.998
Area0.9790.9640.9880.9780.9630.988

ICC: intraclass correlation coefficient, CI: confidence interval..

### References

1. Ertekin C, Aydogdu I. Neurophysiology of swallowing. Clinical Neurophysiology. 2003;114:2226-44.
2. Cabre M, Serra-Prat M, Palomera E, Almirall J, Pallares R, Clave P. Prevalence and prognostic implications of dysphagia in elderly patients with pneumonia. Age Ageing. 2010;39:39-45.
3. Vaiman M, Eviatar E. Surface electromyography as a screening method for evaluation of dysphagia and odynophagia. Head Face Med. 2009;5:9.
4. Vaiman M, Nahlieli O, Segal S, Eviatar E. Electromyography monitoring of patients with salivary gland diseases. Otolaryngol Head Neck Surg. 2005;133(6):869-73.
5. Vaiman M, Nahlieli O, Eliav E. Odynophagia in patients after dental extraction: surface electromyography study. Head and Face. 2007;2:34-40.
6. Vaiman M, Krakovski D, Gavriel H. Swallowing before and after tonsillectomy as evaluated by surface electromyography. Otolaryngol Head Neck Surg. 2007;137(1):138-45.
7. Ertekin C, Tarlaci S, Aydogdu I, Kiylioglu N, Yuceyar N, Esmeli F, et al. Electrophysiological evaluation of pharyngeal phase of swallowing in patients with parkinson’s disease. Mov disord. 2002;17(5):942-9.
8. Ding R, Larson CR, Legemann JA, Rademaker AW. Surface electromyographic and electroglottographic stuidies in normal subjects under two swallow conditions: Normal and during the Mendelsohn manuever. Dysphagia. 2002;17:1-12.
9. Altman KW, Yu GP, Schaeffer SD. Consequence of dysphagia in the hospitalized patient: impact on prognosis and hospital resources. Arch Otolaryngol Head Neck Surg. 2010;136:784-89.
10. Cichero JA, Heaton S, Bassett L. Triaging dysphagia: nurse screening for dysphagia in an acute hospital. J Clin Nurs. 2009;18:1649-59.
11. Kayser-Jones K, Pengilly K. Dysphagia among nursing home residents. Geriatr Nurs. 1999;20:77-84.
12. Steele CM, Greenwood C, Ens I, Robertson C, Seidman-Carlson R. Mealtime difficulties in a home for the aged: not just dysphagia. Dysphagia. 1997;12:45-50.
13. Ekberg O, Hamdy S, Woisard V, Wuttge-Hannig A, Ortega P. Social and psychological burden of dysphagia: Its impact on diagnosis and treatment. Dysphagia. 2002;17:139-46.
14. Sonies BC, Baum BJ. Evaluation of swallowing pathophysiology. Otolaryngol Clin North Am. 1988;21(4):637-48.
15. Vaiman M, Eviatar E, Segal S. Surface electromyographic studies of swallowing in normal subjects: A review of 440 adults. Report 1. Quantitative data: Timing measures. Otolaryngol Head Neck Surg. 2004;131:548-55.
16. Vaiman M, Eviatar E, Segal S. Surface electromyographic studies of swallowing in normal subjects: A review of 440 adults. Report 2. Quantitative data: Amplitude measures. Otolaryngol Head Neck Surg. 2004;131:773-80.
17. Vaiman M, Segal S, Eviatar E. Surface electromyographic studies of swallowing in normal children, age 4-12 years. Int J Pediatr Otorhinolaryngol. 2004;68:65-73.
18. Gupta V, Reddy NP, Canilang EP. Surface EMG measurements at the throat during dry and wet swallowing. Dysphagia. 1996;11:173-9.
19. Huckabee ML, Low IS, McAuliffe MJ. Variability in clinical surface electromyography recording of submental muscle activity in swallowing of healthy participants, Asia Pac J Speech Lang Hear. 2012;15(3):175-86.
20. Inokuchi H, Gonzalez-Fernandez M, Matsuo K, Brodsky MB, Yoda M, Taniguchi H, et al. Electromyography of swallowing with fine wire intramuscular electrodes in healthy human: amplitude difference of selected hyoid muscles. Dysphagia. 2016;31:33-40.
21. Vaiman M, Eviatar E, Segal S. Evaluation of normal deglutition with the help of rectified surface electromyography records. Dysphagia. 2004;19:125-32.
22. Hughes CV, Baum BJ, Fox PC, Marmary Y, Yeh CK, Sonies BC. Oral-pharyngeal dysphagia: a common sequela of salivary gland dysphunction. Dysphagia. 1987;1:173-7.
23. Kantarcigil C, Kim MK, Chang T, Craig BA, Smith A, Lee CH, et al. Validation of a Novel Wearable Electromyography Patch for Monitoring Submental Muscle Activity During Swallowing : A Randomized Crossover Trial. J. Speech Lang. Hear. Res. 2020;63(10):3293-310.

-0001; ():

• ### Dysphagia due to Bilateral Recurrent Laryngeal Nerve Injury after Attempted Suicide by Hanging: A Case Report

2022; 12(2): 138-142

• ### Dysphagia Aortica Initially Misdiagnosed as Poststroke Dysphagia: A Case Report

2022; 12(2): 143-147

more +


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#### 12th Standard English Medium Physics Reduced syllabus Three mark important Questions - 2021(Public Exam )

12th Standard

Reg.No. :
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Physics

Time : 01:00:00 Hrs
Total Marks : 75

3  Marks

25 x 3 = 75
1. Calculate the number of electrons in one coulomb of negative charge.

2. Calculate the electrostatic force and gravitational force between the proton and the electron in a hydrogen atom. They are separated by a distance of 5.3 × 10-11 m. The magnitude of charges on the electron and proton are 1.6 × 10-19 C. Mass of the electron is me = 9.1 × 10-31 kg and mass of proton is mp = 1.6 × 10-27 kg.

3. The following pictures depict electric field lines for various charge configurations.

(i) In figure (a) identify the signs of two
charges and find the ratio $\left| \frac { { q }_{ 1 } }{ { q }_{ 2 } } \right|$
(ii) In figure (b), calculate the ratio of two positive charges and identify the strength of the electric field at three points A, B, and C
(iii) Figure (c) represents the electric field lines for three charges. If q2 = -20 nC, then calculate the values of q1 and q3.

4. Define ‘Electric field’ and discuss its various aspects.

5. Obtain an expression for potential energy due to a collection of three point charges which are separated by finite distances.

6. Obtain the expression for electric field due to an charged infinite plane sheet.

7. Compute the current in the wire if a charge of 120 C is flowing through a copper wire in 1 minute.

8. A copper wire of cross-sectional area 0.5 mm2 carries a current of 0.2 A. If the free electron density of copper is 8.4 × 1028 m-3 then compute the drift velocity of free electrons.

9. A potential difference across 24 Ω resistor is 12 V. What is the current through the resistor?

10. Calculate the equivalent resistance between A and B in the given circuit.

11. Let the magnetic moment of a bar magnet be $\overset { \rightarrow }{ { p }_{ m } }$ whose magnetic length is d = 2l and pole strength is qm. Compute the magnetic moment of the bar magnet when it is cut into two pieces
(a) along its length
(b) perpendicular to its length.

12. The following figure shows the variation of intensity of magnetisation with the applied magnetic field intensity for three magnetic materials X, Y and Z. Identify the material X,Y and Z.

13. Explain the principle and working of a moving coil galvanometer.

14. A cylindrical bar magnet is kept along the axis of a circular solenoid. If the magnet is rotated about its axis, find out whether an electric current is induced in the coil.

15. A solenoid of 500 turns is wound on an iron core of relative permeability 800. The length and radius of the solenoid are 40 cm and 3 cm respectively. Calculate the average emf induced in the solenoid if the current in it changes from 0 to 3 A in 0.4 second.

16. Show that the mutual inductance between a pair of coils is same (M12 = M21).

17. Explain the Maxwell’s modification of Ampere’s circuital law.

18. Light travelling through transparent oil enters in to glass of refractive index 1.5. If the refractive index of glass with respect to the oil is 1.25, what is the refractive index of the oil?

19. What is the radius of the illumination when seen above from inside a swimming pool from a depth of 10 m on a sunny day? What is the total angle of view? [Given, refractive index of water is 4/3]

20. If the focal length is 150 cm for a glass lens, what is the power of the lens?

21. Briefly explain the principle and working of electron microscope.

22. Light of wavelength 390 nm is directed at a metal electrode. To find the energy of electrons ejected, an opposing potential difference is established between it and another electrode. The current of photoelectrons from one to the other is stopped completely when the potential difference is 1.10 V. Determine i) the work function of the metal and ii) the maximum wavelength of light that can eject electrons from this metal

23. Discuss the process of nuclear fusion and how energy is generated in stars?

24. What are the possible harmful effects of usage of Nanoparticles? Why?

25. Discuss diffraction at single slit and obtain the condition for nth minimum.


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Mathematics Help Centre

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The Help Centre is open to students registered in first year mathematics courses.

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# Sum of Product of Sines

• August 23rd 2012, 08:46 PM
sumofsines
Sum of Product of Sines
Hello,

I'm trying to show that $\sum _{j=1}^{N-1} \sin \left(\frac{j \pi y}{N}\right)\sin\left(\frac{j \pi k}{N}\right)=0$, if y does not equal k (with y and k both being integers). I've tried using the trig identity for the product of sines. That gives $\sum _{j=1}^{N-1} \left [\cos\left (\frac{j \pi (y-k)}{N}\right ) - \cos\left (\frac{j \pi (y+k)}{N}\right )\right ]$, but I'm still stuck there.

Any ideas?
• August 23rd 2012, 09:12 PM
MaxJasper
Re: Sum of Product of Sines
Your sum is equal to zero if y<>k and:

$\left\{\left\{\cos \left(\pi \left(-\frac{k}{N}+k-y\right)\right)\to -\cos \left(\pi \left(k-\frac{y}{N}+y\right)\right)+\cos \left(\pi \left(-\frac{k}{N}+k+y\right)\right)+\cos \left(\pi \left(k+\left(\frac{1}{N}-1\right) y\right)\right)\right\}\right\}$


text:
# Nabla hk

Define the $\hat{h}_k$ monomials by $$\left\{ \begin{array}{ll} \hat{h}_0(t,s)=0 \\ \hat{h}_{n+1}(t,s)=\displaystyle\int_s^t \hat{h}_n(\tau,s) \nabla \tau. \end{array} \right.$$
• If $\mathbb{T}=\mathbb{R}$ we get $\hat{h}_k(t,s)=\dfrac{(t-s)^k}{k!}.$
• If $\mathbb{T}=\mathbb{Z}$ we get $\hat{h}_k(t,s)=\dfrac{(t-s)^{\overline{k}}}{k!}$.
• If $\mathbb{T}=\overline{q^{\mathbb{Z}}}, q>1$, then $\hat{h}_k(t,s)=\displaystyle\prod_{r=0}^{k-1} \dfrac{q^rt-s}{\sum_{j=0}^r q^j}$.


text:
# Learning: Parameter Estimation

## Presentation on theme: "Learning: Parameter Estimation"— Presentation transcript:

Learning: Parameter Estimation
Eran Segal Weizmann Institute

Learning Introduction
So far, we assumed that the networks were given Where do the networks come from? Knowledge engineering with aid of experts Automated construction of networks Learn by examples or instances

Learning Introduction
Input: dataset of instances D={d[1],...d[m]} Output: Bayesian network Measures of success How close is the learned network to the original distribution Use distance measures between distributions Often hard because we do not have the true underlying distribution Instead, evaluate performance by how well the network predicts new unseen examples (“test data”) Classification accuracy How close is the structure of the network to the true one? Use distance metric between structures Hard because we do not know the true structure Instead, ask whether independencies learned hold in test data

Prior Knowledge Prespecified structure Prespecified variables
Learn only CPDs Prespecified variables Learn network structure and CPDs Hidden variables Learn hidden variables, structure, and CPDs Complete/incomplete data Missing data Unobserved variables

Learning Bayesian Networks
X1 X2 Inducer Data Prior information Y P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98

Known Structure, Complete Data
Goal: Parameter estimation Data does not contain missing values X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y x10 x21 y0 x11 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data

Unknown Structure, Complete Data
Goal: Structure learning & parameter estimation Data does not contain missing values X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y x10 x21 y0 x11 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data

Known Structure, Incomplete Data
Goal: Parameter estimation Data contains missing values Example: Naïve Bayes X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y ? x21 y0 x11 x10 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data

Unknown Structure, Incomplete Data
Goal: Structure learning & parameter estimation Data contains missing values X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y ? x21 y0 x11 x10 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data

Parameter Estimation Input Goal: Learn CPD parameters
Network structure Choice of parametric family for each CPD P(Xi|Pa(Xi)) Goal: Learn CPD parameters Two main approaches Maximum likelihood estimation Bayesian approaches

Biased Coin Toss Example
Coin can land in two positions: Head or Tail Estimation task Given toss examples x[1],...x[m] estimate P(H)= and P(T)=1- Assumption: i.i.d samples Tosses are controlled by an (unknown) parameter  Tosses are sampled from the same distribution Tosses are independent of each other

Biased Coin Toss Example
Goal: find [0,1] that predicts the data well “Predicts the data well” = likelihood of the data given  Example: probability of sequence H,T,T,H,H 0.2 0.4 0.6 0.8 1 L(D:)

Maximum Likelihood Estimator
Parameter  that maximizes L(D:) In our example, =0.6 maximizes the sequence H,T,T,H,H 0.2 0.4 0.6 0.8 1 L(D:)

Maximum Likelihood Estimator
General case Observations: MH heads and MT tails Find  maximizing likelihood Equivalent to maximizing log-likelihood Differentiating the log-likelihood and solving for  we get that the maximum likelihood parameter is:

Sufficient Statistics
For computing the parameter  of the coin toss example, we only needed MH and MT since  MH and MT are sufficient statistics

Sufficient Statistics
A function s(D) is a sufficient statistics from instances to a vector in k if for any two datasets D and D’ and any  we have Datasets Statistics

Sufficient Statistics for Multinomial
A sufficient statistics for a dataset D over a variable Y with k values is the tuple of counts <M1,...Mk> such that Mi is the number of times that the Y=yi in D Sufficient statistic Define s(x[i]) as a tuple of dimension k s(x[i])=(0,...0,1,0,...,0) (1,...,i-1) (i+1,...,k)

Sufficient Statistic for Gaussian
Gaussian distribution: Rewrite as  sufficient statistics for Gaussian: s(x[i])=<1,x,x2>

Maximum Likelihood Estimation
MLE Principle: Choose  that maximize L(D:) Multinomial MLE: Gaussian MLE:

MLE for Bayesian Networks
Parameters x0, x1 y0|x0, y1|x0, y0|x1, y1|x1 Data instance tuple: <x[m],y[m]> Likelihood X x0 x1 0.7 0.3 X Y Y X y0 y1 x0 0.95 0.05 x1 0.2 0.8  Likelihood decomposes into two separate terms, one for each variable

MLE for Bayesian Networks
Terms further decompose by CPDs: By sufficient statistics where M[x0,y0] is the number of data instances in which X takes the value x0 and Y takes the value y0 MLE

MLE for Bayesian Networks
Likelihood for Bayesian network  if Xi|Pa(Xi) are disjoint then MLE can be computed by maximizing each local likelihood separately

MLE for Table CPD BayesNets
Multinomial CPD For each value xX we get an independent multinomial problem where the MLE is

MLE for Tree CPDs Assume tree CPD with known tree structure Y Z X
Terms for <y0,z0> and <y0,z1> can be combined Y y0 y1 Z x0|y0,x1|y0 z0 z1 Optimization can be done by leaves x0|y1,z1,x1|y1,z1 x0|y1,z1,x1|y1,z1

MLE for Tree CPD BayesNets
Tree CPD T, leaves l For each value lLeaves(T) we get an independent multinomial problem where the MLE is

Limitations of MLE Two teams play 10 times, and the first wins 7 of the 10 matches  Probability of first team winning = 0.7 A coin is tosses 10 times, and comes out ‘head’ 7 of the 10 tosses  Probability of head = 0.7 Would you place the same bet on the next game as you would on the next coin toss? We need to incorporate prior knowledge Prior knowledge should only be used as a guide

Bayesian Inference … Assumptions
Given a fixed  tosses are independent If  is unknown tosses are not marginally independent – each toss tells us something about  The following network captures our assumptions X[1] X[M] X[2]

For a uniform prior, posterior is the normalized likelihood
Bayesian Inference Joint probabilistic model Posterior probability over  X[1] X[2] X[M] Likelihood Prior For a uniform prior, posterior is the normalized likelihood Normalizing factor

Bayesian Prediction Predict the data instance from the previous ones
Solve for uniform prior P()=1 and binomial variable

Example: Binomial Data
Prior: uniform for  in [0,1] P() = 1  P( |D) is proportional to the likelihood L(D:) MLE for P(X=H) is 4/5 = 0.8 Bayesian prediction is 5/7 0.2 0.4 0.6 0.8 1 (MH,MT ) = (4,1)

Dirichlet Priors A Dirichlet prior is specified by a set of (non-negative) hyperparameters 1,...k so that  ~ Dirichlet(1,...k) if where and Intuitively, hyperparameters correspond to the number of imaginary examples we saw before starting the experiment

Dirichlet Priors – Example
5 Dirichlet(1,1) Dirichlet(2,2) 4.5 Dirichlet(0.5,0.5) Dirichlet(5,5) 4 3.5 3 2.5 2 1.5 1 0.5 0.2 0.4 0.6 0.8 1

Dirichlet Priors Dirichlet priors have the property that the posterior is also Dirichlet Data counts are M1,...,Mk Prior is Dir(1,...k)  Posterior is Dir(1+M1,...k+Mk) The hyperparameters 1,…,K can be thought of as “imaginary” counts from our prior experience Equivalent sample size = 1+…+K The larger the equivalent sample size the more confident we are in our prior

Effect of Priors Prediction of P(X=H) after seeing data with MH=1/4MT as a function of the sample size 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 20 40 60 80 100 0.1 0.6 Different strength H + T Fixed ratio H / T Fixed strength H + T Different ratio H / T

Effect of Priors (cont.)
In real data, Bayesian estimates are less sensitive to noise in the data 0.7 MLE Dirichlet(.5,.5) Dirichlet(1,1) Dirichlet(5,5) Dirichlet(10,10) 0.6 0.5 P(X = 1|D) 0.4 0.3 0.2 N 0.1 5 10 15 20 25 30 35 40 45 50 1 Toss Result N

General Formulation Joint distribution over D,
Posterior distribution over parameters P(D) is the marginal likelihood of the data As we saw, likelihood can be described compactly using sufficient statistics We want conditions in which posterior is also compact

Conjugate Families A family of priors P(:) is conjugate to a model P(|) if for any possible dataset D of i.i.d samples from P(|) and choice of hyperparameters  for the prior over , there are hyperparameters ’ that describe the posterior, i.e., P(:’) P(D|)P(:) Posterior has the same parametric form as the prior Dirichlet prior is a conjugate family for the multinomial likelihood Conjugate families are useful since: Many distributions can be represented with hyperparameters They allow for sequential update within the same representation In many cases we have closed-form solutions for prediction

Bayesian Estimation in BayesNets
Bayesian network for parameter estimation Bayesian network X X X[1] X[2] X[M] Y Y[1] Y[2] Y[M] Y|X Instances are independent given the parameters (x[m’],y[m’]) are d-separated from (x[m],y[m]) given  Priors for individual variables are a priori independent Global independence of parameters

Bayesian Estimation in BayesNets
Bayesian network for parameter estimation Bayesian network X X X[1] X[2] X[M] Y Y[1] Y[2] Y[M] Y|X Posteriors of  are independent given complete data Complete data d-separates parameters for different CPDs As in MLE, we can solve each estimation problem separately

Bayesian Estimation in BayesNets
Bayesian network for parameter estimation Bayesian network X X X[1] X[2] X[M] Y Y[1] Y[2] Y[M] Y|X=0 Y|X=1 Posteriors of  are independent given complete data Also holds for parameters within families Note context specific independence between Y|X=0 and Y|X=1 when given both X and Y

Bayesian Estimation in BayesNets
Bayesian network for parameter estimation Bayesian network X X X[1] X[2] X[M] Y Y[1] Y[2] Y[M] Y|X=0 Y|X=1 Posteriors of  can be computed independently For multinomial Xi|pai posterior is Dirichlet with parameters Xi=1|pai+M[Xi=1|pai],..., Xi=k|pai+M[Xi=k|pai]

Assessing Priors for BayesNets
We need the (xi,pai) for each node xi We can use initial parameters 0 as prior information Need also an equivalent sample size parameter M’ Then, we let (xi,pai) = M’  P(xi,pai|0) This allows to update a network using new data Example network for priors P(X=0)=P(X=1)=0.5 P(Y=0)=P(Y=1)=0.5 M’=1 Note: (x0)=0.5 (x0,y0)=0.25 X Y

Case Study: ICU Alarm Network
The “Alarm” network 37 variables Experiment Sample instances Learn parameters MLE Bayesian PCWP CO HRBP HREKG HRSAT ERRCAUTER HR HISTORY CATECHOL SAO2 EXPCO2 ARTCO2 VENTALV VENTLUNG VENITUBE DISCONNECT MINVOLSET VENTMACH KINKEDTUBE INTUBATION PULMEMBOLUS PAP SHUNT ANAPHYLAXIS MINOVL PVSAT FIO2 PRESS INSUFFANESTH TPR LVFAILURE ERRBLOWOUTPUT STROEVOLUME LVEDVOLUME HYPOVOLEMIA CVP BP

Case Study: ICU Alarm Network
MLE 1.4 Bayes w/ Uniform Prior, M'=5 Bayes w/ Uniform Prior, M'=10 1.2 Bayes w/ Uniform Prior, M'=20 Bayes w/ Uniform Prior, M'=50 1 0.8 KL Divergence 0.6 0.4 0.2 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 M MLE performs worst Prior M’=5 provides best smoothing

Parameter Estimation Summary
Estimation relies on sufficient statistics For multinomials these are of the form M[xi,pai] Parameter estimation Bayesian methods also require choice of priors MLE and Bayesian are asymptotically equivalent Both can be implemented in an online manner by accumulating sufficient statistics MLE Bayesian (Dirichlet)


text:
Enhanced protein fold recognition through a novel data integration approach
Yiming Ying, Kaizhu Huang and Colin Campbell
BMC Bioinformatics Volume 10, Number 267, 2009.

Abstract

In this paper we consider the problem of integrating multiple data sources using a kernel-based approach. We propose a novel information-theoretic approach based on a Kullback-Leibler (KL) divergence between the output kernel matrix and the input kernel matrix so as to integrate heterogeneous data sources. One of the most appealing properties of this approach is that it can easily cope with multi-class classification and multi-task learning by an appropriate choice of the output kernel matrix. Based on the position of the output and input kernel matrices in the KL-divergence objective, there are two formulations which we respectively refer to as {\em MKLdiv-dc} and {\em MKLdiv-conv}. We propose to efficiently solve MKLdiv-dc by a difference of convex (DC) programming method and MKLdiv-conv by a projected gradient descent algorithm. The effectiveness of the proposed approaches is evaluated on a benchmark dataset for protein fold recognition and a yeast protein function prediction problem.