Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms

被引：4

作者：

Lee, Jaewon ^{[1
]}

Shin, Miyoung ^{[2
]}

机构：

[1] Kyungpook Natl Univ, Sch Elect & Elect Engn, Biointelligence & Data Min Lab, Daegu 41566, South Korea

[2] Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea

来源：

BIOENGINEERING-BASEL | 2023年 / 10卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

ECG rhythms; rhythm classification; deep learning; convolutional neural network (CNN);

D O I：

10.3390/bioengineering10020196

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve these issues, we proposed a novel method that distinguishes a given ECG rhythm using a beat score map (BSM) image. Through the proposed method, the associations between beats and previously used features, such as the R-R interval, were considered. Rhythm classification was implemented by training a convolutional neural network model and using transfer learning with the created BSM image. As a result, the proposed method for ECG rhythms with small data samples showed significant results. It also showed good performance in differentiating atrial fibrillation (AFIB) and atrial flutter (AFL) rhythms, which are difficult to distinguish due to their similar characteristics. The performance for rhythms with a small number of samples of the proposed method is 20% better than an existing method. In addition, the performance based on the F-1 score for classifying AFIB and AFL of the proposed method is 30% better than the existing method. This study solved the previous limitations caused by small sample numbers and similar rhythms.

引用

页数：13

共 61 条

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