Key result
The proposed MWCapsuleNet combined with Seq2seq achieved an accuracy of 99.88% for arrhythmia classification under sample imbalance.
Why the study?
The study was designed to construct a method to alleviate the problem of sample imbalance in classification, particularly for arrhythmia classification, without using data enhancement.
A novel machine learning approach combining MLP, WCapsule, and Seq2Seq networks achieved 99.88% accuracy in ECG arrhythmia classification, effectively addressing sample imbalance.
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement. Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method’s performance is further evaluated. Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm. Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples.
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Zhou et al. (2023) studied Arrhythmia (n=48). MWCapsuleNet combined with Seq2seq vs. Baseline machine learning and deep learning models was evaluated on Accuracy (ACC) of heartbeat classification. The proposed MWCapsuleNet combined with Seq2seq achieved an accuracy of 99.88% for arrhythmia classification under sample imbalance.
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