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Resource-Efficient Heartbeat Classification Using Multi-Feature Fusion and Bidirectional LSTM
Using a Bi-LSTM network, rather than a conventional LSTM network, resulted in higher accuracy (33. 8\% vs 21. 8\%) with a 28\% reduction in required network parameters for the RBBB class.
EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification.
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