A Classification Method of Arrhythmia Based on Adaboost Algorithm. (November 2020)

• Record Type: Journal Article
• Authors: Zhang, Bing; Wen, Jingye; Ren, Huihui
• Is Part Of: Journal of physics. Volume 1682(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1682/1/012060
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: An arrhythmia classification model based on an adaptive boosting algorithm is proposed in this paper. According to the AAMI standard, 15 kinds of abnormal cardiac rhythms are grouped and the datasets are segmented by the non-crossover method. The electrocardiogram (ECG) signals are denoised by the filter method, and then divided into fixed-length ECG beats, and five features are extracted from time-domain and frequency-domain. Then, the base classifier of the algorithm and its optimal algorithm parameters is selected to realize the multi-classification of cardiac anomalies, aiming at mining hidden knowledge from human physiological data to detect human health status, making the diagnosis process more automatic, efficient, and intelligent.