Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting.
10.7507/1001-5515.202101054
- Author:
Huijun YAN
1
;
Site MO
1
;
Hua HUANG
1
;
Yan LIU
1
Author Information
1. School of Electrical Engineering, Sichuan University, Chengdu 610065, P.R.China.
- Publication Type:Journal Article
- Keywords:
R wave extraction;
arrhythmia;
autoregressive moving average model;
heart beat segmentation;
support vector machine
- MeSH:
Algorithms;
Atrial Fibrillation;
Electrocardiography;
Heart Rate;
Humans;
Signal Processing, Computer-Assisted;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2021;38(5):848-857
- CountryChina
- Language:Chinese
-
Abstract:
The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the
