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Maghsoudpour, Adel, Dorostghol, Ali, Ghaffari, Ali, NikkhahBahrami, Mansour. (1403). Prediction of sudden cardiac death based on fundamental changes in nonlinear characteristics of cardiac signals. فناوری آموزش, (), -. doi: 10.22061/jcarme.2024.9627.2291
Adel Maghsoudpour; Ali Dorostghol; Ali Ghaffari; Mansour NikkhahBahrami. "Prediction of sudden cardiac death based on fundamental changes in nonlinear characteristics of cardiac signals". فناوری آموزش, , , 1403, -. doi: 10.22061/jcarme.2024.9627.2291
Maghsoudpour, Adel, Dorostghol, Ali, Ghaffari, Ali, NikkhahBahrami, Mansour. (1403). 'Prediction of sudden cardiac death based on fundamental changes in nonlinear characteristics of cardiac signals', فناوری آموزش, (), pp. -. doi: 10.22061/jcarme.2024.9627.2291
Maghsoudpour, Adel, Dorostghol, Ali, Ghaffari, Ali, NikkhahBahrami, Mansour. Prediction of sudden cardiac death based on fundamental changes in nonlinear characteristics of cardiac signals. فناوری آموزش, 1403; (): -. doi: 10.22061/jcarme.2024.9627.2291

Prediction of sudden cardiac death based on fundamental changes in nonlinear characteristics of cardiac signals

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 اردیبهشت 1403    اصل مقاله (995.58 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2024.9627.2291
نویسندگان
Adel Maghsoudpour* 1؛ Ali Dorostghol2؛ Ali Ghaffari3؛ Mansour NikkhahBahrami4
1Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3Department of Mechanical Engineering, K.N. Toosi University of Technology. NO. 19 Pardis St., Mollasadra Ave., Vanak Sq., Tehran, Iran.
4Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
تاریخ دریافت: 23 بهمن 1401،  تاریخ بازنگری: 24 اسفند 1402،  تاریخ پذیرش: 19 اردیبهشت 1403 
چکیده
To quickly detect sudden Cardiac Death (SCD), it's decisive to gather suitable information and enhance the accuracy of the diagnosis algorithms. Consequently, In the present study the heart rate variability (HRV) signal of subjects who experienced sudden cardiac death (SCD) was studied. We looked at people's heart signals for one hour before something happened to see if there were any noticeable changes. The patients' HRV signals are segregated into 5-minute parts in the suggested approach. Each section is divided into four shorter signals. Thereupon, The energy and instant amplitude of each sub-signal are examined. The information flows between signal strengths and measuring the complexity of energy sub-signals would be checked. A significant change from its former section is identified. A support vector machine (SVM) classifier benefits from detecting individuals exposed to SCD by considering significant changes as indicators of the SCD process. It can anticipate SCD 15 minutes before it happens. Not restricted to any special subclass of cardiac diseases, this technique has priority. To evaluate the algorithm's specificity, it has been used not only with patients having SCD but also with individuals who are healthy, as well as those with coronary artery disease (CAD) and congestive heart failure (CHF), analyzing their HRV signals. The specificity values for normal, CHF, and CAD patients are 100%, 93.3%, and 95.6%, respectively, in the results.
کلیدواژه‌ها
Sample entropy؛ Teager-Kaiser operator؛ Transfer entropy؛ Local characteristic decomposition؛ Heart rate variability
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