Lightweight beat score map method for electrocardiogram-based arrhythmia classification

초록

We recently investigated beat score map (BSM)-based methods for electrocardiogram (ECG)-based arrhythmia classification. Although BSM-based methods show impressive performance, they are somewhat resourceintensive owing to the arrangement of beat score vectors generated from 1D ECG sequences with zeropadding across time points. To address this issue, we propose a lightweight BSM (Lw-BSM) method that significantly reduces the size of the original BSM while capturing the characteristics of beat arrangement patterns as does the original BSM. Specifically, two types of Lw-BSMs are generated without zero-padding and evaluated for multiclass arrhythmia prediction. Experimental results on two public datasets, MIT-BIH and SPH, demonstrate that arrhythmia classification using Lw-BSM images is quite comparable to that using the original BSM images as an input to CNN-based classification models. At the same time, the image size can be reduced significantly. Moreover, it is observed that this approach is almost insensitive to the selection of the R-peak detection algorithm, showing stable performance across different R-peak algorithms.

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