ANALYSIS ON DEEP LEARNING METHODS FOR ECG BASED CARDIOVASCULAR DISEASE PREDICTION

被引：11

作者：

Kusuma, S. ^{[1
]}

Udayan, Divya J. ^{[2
]}

机构：

[1] VIT Vellore, Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India

[2] VIT Vellore, Vellore Inst Technol, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India

来源：

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE | 2020年 / 21卷 / 01期

关键词：

Deep learning; !text type='Python']Python[!/text; CVD; ECG; ARRHYTHMIA DETECTION; CLASSIFICATION; NETWORK;

D O I：

10.12694/scpe.v21i1.1640

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The cardiovascular related diseases can however be controlled through earlier detection as well as risk evaluation and prediction. In this paper the application of deep learning methods for CVD diagnosis using ECG is addressed and also discussed the deep learning with Python. A detailed analysis of related articles has been conducted. The results indicate that convolutional neural networks are the most widely used deep learning technique in the CVD diagnosis. This research paper looks into the advantages of deep learning approaches that can be brought by developing a framework that can enhance prediction of heart related diseases using ECG.

引用

页码：127 / 136

页数：10

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