ECG Arrhythmia Classification Using Least Squares Twin Support Vector Machines

ECG Arrhythmia Classification Using Least Squares Twin Support Vector Machines
بیست و ششمین کنفرانس مهندسی برق ایران
مشهد
تاریخ کنفرانس: 
سه شنبه - ۱۸ ارديبهشت ۱۳۹۷ تا پنجشنبه - ۲۰ ارديبهشت ۱۳۹۷
مؤسسه برگزارکننده: 
چکیده

Heart disease is one of the most common causes of death.‎ Rapid diagnosis of patients with these diseases can greatly prevent them from sudden death.‎ Today, the diagnosis of heart diseases is done by cardiologist, while achieving an automatic and accurate method for diagnosing has become a challenging issue in this area.‎ Because small changes in the electrocardiogram signals are not recognizable with eyes, and visual disorders may be affected, artificial intelligence and machine learning algorithms can be the solution.‎ In this paper, we use the Least Squares Twin Support Vector Machine, which unlike ordinary support vector machine, is based on a Non-parallel margin.‎ The results show that the method of this article is better than previous methods, and more accurate and faster for diagnosing arrhythmia.‎

استناد: 

Refahi, Mohammad Saleh, Jalal A.‎ Nasiri, and Seyed Mohammad Ahadi.‎ 2018.‎ ECG Arrhythmia Classification Using Least Squares Twin Support Vector Machines. Paper Presented in 26th Iranian Conference on Electrical Engineering (ICEE2018), Mashhad.‎

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