عنوان مقاله فارسی: عملکرد طبقه بندی شده شخصی سازی ضربان قلب با عملکرد بالا برای سیگنال ECG طولانی مدت

عنوان مقاله لاتین: High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal

نویسندگان: Pengfei Li; Yu Wang; Jiangchun He; Lihua Wang; Yu Tian; Tian-shu Zhou; Tianchang Li; Jing-song Li

تعداد صفحات: 8

سال انتشار: 2017

زبان: لاتین

. Abstract:

Long-term electrocardiogram (ECG) has become one of the important diagnostic assist methods in clinical cardiovascular domain. Long-term ECG is primarily used for the detection of various cardiovascular diseases that are caused by various cardiac arrhythmia such as myocardial infarction, cardiomyopathy, and myocarditis. In the past few years, the development of an automatic heartbeat classification method has been a challenge. With the accumulation of medical data, personalized heartbeat classification of a patient has become possible. For the long-term data accumulation method, such as the holter, it is difficult to obtain the analysis results in a short time using the original method of serial design. The pressure to develop a personalized automatic classification model is high. To solve these challenges, this paper implemented a parallel general regression neural network (GRNN) to classify the heartbeat, and achieved a 95% accuracy according to the Association for the Advancement of Medical Instrumentation. We designed an online learning program to form a personalized classification model for patients. The achieved accuracy of the model is 88% compared to the specific ECG data of the patients. The efficiency of the parallel GRNN with GTX780Ti can improve by 450 times.