Key result
Deep residual network achieves ~99.6% arrhythmia classification accuracy on the MIT-BIH database.
Why the study?
Arrhythmia classification using ECG is important for identifying arrhythmia diseases, but frequent variance in ECG waveforms makes achieving high classification accuracy challenging.
Does a deep residual network improve arrhythmia classification accuracy on ECGs compared to state-of-the-art deep learning methods?
Does a deep residual network improve arrhythmia classification accuracy on ECGs compared to state-of-the-art deep learning methods?
A deep residual network with improved residual blocks achieves highly accurate arrhythmia classification on ECG data, outperforming existing deep learning methods.
Supports arrhythmia detection model refinement; leaves open prospective clinical validation before any practice impact.
Arrhythmia classification with electrocardiogram (ECG) is of great importance for the identification of arrhythmia diseases. However, since the variance of ECG signal in wave appears frequently, it is still a very challenging task to obtain a very good classification result. In this paper, an arrhythmia classification with ECG based on deep residual networks is proposed, of which two improved residual blocks are used to combine soft and hard subsampling. With such blocks, the network can well hold spatial information and improve the classification performance with a simple model structure. Experiments on the MIT-BIH arrhythmia database show that the proposed method obtained an average classification accuracy of 99.59% and an average classification specificity 99.63%, which are 0.26% and 0.57% higher than that of the most state-of-art method based on deep learning.
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Shi et al. (2021) studied Arrhythmia. Deep residual networks with improved residual blocks combining soft and hard subsampling vs. State-of-the-art methods based on deep learning was evaluated on Average classification accuracy. A deep residual network combining soft and hard subsampling achieved an average arrhythmia classification accuracy of 99.59% and specificity of 99.63% on the MIT-BIH database.
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