Open AccessA Classification Method of Arrhythmia Based on Adaboost Algorithm
Author(s) -
Bing Zhang,
Jingye Wen,
Huihui Ren
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012060
Subject(s) - adaboost , boosting (machine learning) , computer science , pattern recognition (psychology) , artificial intelligence , classifier (uml) , cardiac arrhythmia , frequency domain , crossover , algorithm , computer vision , medicine , atrial fibrillation , cardiology
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.