Key result
Novel two-lead CNN-LSTM system outperforms state-of-the-art methods for inter-patient arrhythmia classification.
Why the study?
Inter-patient arrhythmia classification remains difficult and most previous work has focused on intra-patient conditions without following AAMI standards.
A novel machine learning system combining CNN, LSTM, and traditional features improves inter-patient arrhythmia classification on multi-lead ECGs compared to existing methods.
May aid inter-patient arrhythmia classification on multi-lead ECG; leaves open prospective validation before clinical use.
Arrhythmia classification is useful during heart disease diagnosis. Although well-established for intra-patient diagnoses, inter-patient arrhythmia classification remains difficult. Most previous work has focused on the intra-patient condition and has not followed the Association for the Advancement of Medical Instrumentation (AAMI) standards. Here, we propose a novel system for arrhythmia classification based on multi-lead electrocardiogram (ECG) signals. The core of the design is that we fuse two types of deep learning features with some common traditional features and select discriminating features using a binary particle swarm optimization algorithm (BPSO). Then, the feature vector is classified using a weighted support vector machine (SVM) classifier. For a better generalization of the model and to draw fair comparisons, we carried out inter-patient experiments and followed the AAMI standards. We found that, when using common metrics aimed at multi-classification either macro- or micro-averaging, our system outperforms most other state-of-the-art methods.
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Chu et al. (2019) studied Arrhythmia. Two-lead arrhythmia classification system based on CNN and LSTM vs. Other state-of-the-art methods was evaluated on Arrhythmia classification performance. A novel two-lead arrhythmia classification system based on CNN and LSTM outperformed most other state-of-the-art methods for inter-patient arrhythmia classification.
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