Key result
An arrhythmia classification model based on an adaptive boosting algorithm was proposed to realize the multi-classification of 15 kinds of abnormal cardiac rhythms.
Why the study?
The study was conducted to mine hidden knowledge from physiological data to detect health status and make arrhythmia diagnosis more automatic, efficient, and intelligent.
An Adaboost-based machine learning model can be used to classify 15 types of arrhythmias from ECG signals, potentially automating and improving the efficiency of cardiac anomaly diagnosis.
May aid automated arrhythmia detection; hypothesis-generating without prospective clinical validation.
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.
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Zhang et al. (2020) studied Arrhythmia. Adaptive boosting (Adaboost) algorithm was evaluated on Multi-classification of cardiac anomalies. An arrhythmia classification model based on an adaptive boosting algorithm was proposed to realize the multi-classification of 15 kinds of abnormal cardiac rhythms.
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