مدل های یادگیری ماشین مخصوص بیمار برای طبقه بندی سیگنال ECG
Abstract
1- Introduction
2- Related work
3- Material and methods
4- Experimental Results
5- Conclusion
References
Abstract
Arrhythmia is one of the major cause of deaths across the globe. Almost 17.9 million deaths are caused due to cardiovascular diseases. In order to reduce this much mortality rate, the cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. In this study, a new ensemble based support vector machine (SVM) classifier was proposed to classify heartbeat into four classes from MIT-BIH arrhythmia database. The results were compared with other classifiers that are SVM, Random Forest (RF), K-Nearest Neighbours (KNN), and Long Short Term Memory network. The four features were extracted from the ECG signals that were used by the classifiers are Wavelets, high order statistics, R-R intervals and morphological features. An ensemble of SVMs obtained the best result with an overall accuracy of 94.4%.
Introduction
The prime cause of deaths across the world according to the report of WHO are cardiovascular diseases (CVDs): Annually, more people die from cardiac diseases than from any other disease. In 2016, approximately 18 million people died because of cardiac-related diseases, which reflects 31% of all the deaths globally. 85% of the deaths among this 31% are due to heart attack and stroke. An approximate of three-fourths of cardiac deaths take place in low-income and middle-income countries [1]. In 2015, 82% of the 17 million premature deaths due to non- communicable diseases are in low and middle-income countries, and the rest are caused by CVDs. The leading cause of CVDs is a long-term effect of cardiac arrhythmias. When the electrical signal, to the heart that coordinate heartbeats don’t work properly, Arrhythmias occur [2].