Signal and Data Processing
پردازش علائم و دادهها
JSDP
Engineering & Technology
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2017
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طبقهبندی آریتمیهای قلبی مبتنی بر ترکیب نتایج شبکههای عصبی با نظریه شواهد دمپستر- شفر
Classification of Cardiac Arrhythmias based on combination of the results of Neural Networks using Dempster-Shefer Evidence Theory
مقالات گروه علائم حیاتی ( مرتبط با مهندسی پزشکی)
Paper
پژوهشي
Research
<p dir="RTL"><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;">آریتمیهای قلبی یکی از شایعترین بیماریهای قلبی است که ممکن است سبب مرگ بیمار شود. ازاینرو شناسایی آریتمیهای قلبی بسیار مهم است. در این مقاله برای دستهبندی آریتمیهای قلبی در سه طبقه </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">PAC</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;">، </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">PVC</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> و </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">Normal</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> روشی مبنی بر ترکیب طبقهبندیکنندهها با استفاده از نظریه شواهد لحاظ شده است. بدین شکل که ابتدا پیکهای </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">R</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> در </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">ECG</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> شناسایی شد؛ سپس ویژگیهای خطی </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">ECG</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> شامل </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">RMSSD</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;">، </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">SDNN</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> و </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">HR Mean</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> و همچنین ویژگی غیر خطی آن با استفاده از </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">SVD</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> بهدست آمد. ترکیب ویژگیهای بهدستآمده به شبکههای عصبی </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">MLP</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;">، </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">Cascade Feed Forward</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> و </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">RBF</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> داده شد. اصل عدم قطعیت در مورد پاسخ آنها بررسی و درنهایت پاسخ این طبقهبندیکنندهها با استفاده از نظریه شواهد با یکدیگر ترکیب شدند. جهت پردازش </span></span></strong><strong><span dir="LTR"><span style="font-family:times new roman,serif;"><span style="font-size:8.0pt;">ECG</span></span></span></strong><strong><span style="font-family:b nazanin;"><span style="font-size:10.0pt;"> نیاز به حذف نوفه نبوده و روش پیشنهادی توانسته است در حضور نوفه، نوع آریتمی قلبی را در بهترین حالت با حساسیت 98 % تشخیص دهد.</span></span></strong><br>
</p>
<p><strong><span dir="LTR">Cardiac arrhythmias are one of the most common heart diseases that may cause the death of the patient. Therefore, it is extremely important to detect cardiac arrhythmias. 3 categories of arrhythmia, namely, PAC, PVC, and normal are considered in this paper based on classifier fusion using evidence theory.</span></strong><br>
<strong>In this study, at first a sample is carrying out the ECG signal with 250 point. Moreover, in each of the sampling, the maximum values will be obtained. Then, the average of the calculated values would be considered as adaptive thresholding and the total signals are multiplied by the inverse adaptive thresholding<span dir="RTL">.</span> After fixing the adaptive thresholding at number one, total resulting signal is becoming the power of 2. In this situation, the amounts smaller than one, are weakened and the larger than one amounts are reinforced. The smaller amount is removed and other amounts are held</strong><strong>. </strong><strong>Then, the maximum in each of the sampling is considered.</strong><br>
<strong>In sampling areas that there is no peak, some maximum can be identified with zero value that these points should be removed from the set maximum. To find the maximum point where the maximum is close to the borders of sampling, two peaks may be placed in one field. This problem leads to removing one peak and non-recognition of the smaller peak. Some peaks near the border of sampling, for example the previous or next point on the border may be identified as the peak which eliminates the major peak and identifies the unrealistic peak. To solve this problem, the 80-point sampling is performed around each detected peak and the maximum value is obtained at the sampling areas. In this way, the correct peaks are identified and the wrong one will be deleted</strong>.<br>
<strong><span dir="LTR">In some parts, the peak signal is not quite sharp, and maybe two or more points that are adjacent to each other with the same value, will be considered as a peak. In other words, a closed peak is detected several times, which leads to detection of extra and incorrect peaks. In these circumstances, according to an amount that only belongs to one peak, just one of them should be considered and the other should be removed. After these steps, an obtained signal which includes peaks R, is compared with the original signal. To achieve the correct answer, it changes the number of sampling points and each time the result is compared with the previous values and with the original signal, too, until finally the major peaks will be identified.</span></strong><br>
<strong>Then, HRV signal be will calculated. Linear properties contain root mean square of successive differences between normal intervals (RMSSD) and standard deviation of normal to normal intervals in a row (SDNN) and also heart rate (HR Mean) are calculated.</strong><br>
<strong>Around each peak, 81 points window is inserted. These points for each peak is in one row. So resulting matrix (X) has 81 columns and its rows are the number of R peaks. SVD of matrix(X) is calculated. The obtained Matrix S will include the individual values. These singular signal values are non-linear features. If all used values are single, they can eclipse the linear features which will lead to the lack of features’ effect. Because of this reason, it is used only from the largest single value as a non-linear feature.</strong><br>
<strong>The combination of linear and non-linear characteristics as input is applied to </strong><strong>MLP, Cascade Feed Forward and RBF neural networks </strong><strong>and every (single) answer is studied. The answers for each class have a level of probability that any classifier can independently be taken to the classification of cardiac arrhythmias. A class that has the greatest probability is allocated to the data. These probabilities show the uncertainty of the answers.</strong><br>
<strong>Each of the classifiers is considered as a witness. All the possibilities for different classes of each witness uncertainties function are modeled and crime function is defined. In other words, belief structure is formed for evidence. At this stage, by combined Demster law, the mass functions will combine together. In this situation, the level of uncertainty is much reduced and the class with the highest crime will be selected as the answer.</strong><br>
<strong>According to the survey results, the combination of linear and non-linear characteristics for training and testing the neural networks classifiers has increased the accuracy of the answer. In other words, the extraction of more features leads to better training the neural networks and increases the accuracy of the classifiers.</strong><br>
<strong>It can be noted that the using classifiers uncertainty principle and combining them by using the evidence theory has increased the accuracy of the final classification. The results of this study show that the proposed method was able to </strong><strong>classify cardiac arrhythmias in the presence of noise and provided an acceptable answer for the intended issue. In sum, the proposed method has been able to classify 3 categories of cardiac arrhythmia such as PVC, PAC and NORMAL with high accuracy. This is performed in the best situation with sensitivity greater than 0/98.</strong><br>
</p>
سیگنال الکتروکاردیوگرام, طبقهبندیکنندهها, شبکههای عصبی, نظریه شواهد
ECG signal, Classifier, Neural Networks, Evidence theory
25
42
http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-626-3&slc_lang=fa&sid=1
Jamal
Ghasemi
جمال
قاسمی
j.ghasemi@umz.ac.ir
10031947532846006052
10031947532846006052
Yes
University of Mazandaran
دانشگاه مازندران
Somayeh
Kord
سمیه
کرد
somayye.kord@yahoo.com
10031947532846006053
10031947532846006053
No
Azad University, Noor Branch
دانشگاه آزاد واحد نور
Mohamad
Gholami
محمد
غلامی
m.gholami@umz.ac.ir
10031947532846006054
10031947532846006054
No
University of Mazandaran
دانشگاه مازندران