Key result
Novel 13-layer deep dynamic neural network proves feasible and effective for ECG signal pattern classification.
Why the study?
ECG signals are time-varying signals characterized by variability, instability, and noise, posing challenges for automated learning and classification.
A novel deep dynamic neural network model combining CNN, LSTM, and wavelet blocks demonstrates feasibility and effectiveness for ECG signal classification.
May advance ECG classification research; leaves open clinical validation before any practice change.
The electrocardiogram (ECG) signal is a kind of time-varying signal, which has the characteristics and difficulties of variability, instability, and noise. Aiming at that, this paper put forward a novel 13-layer deep dynamic neural network model (DDNN) for the ECG signal learning and classification. The proposed DDNN model is a dynamic hybrid deep learning model. It includes a wavelet block, a convolutional block, a recurrent block, and a classification block, which combines the learning property and classification mechanism of convolutional neural network for the large-scale data sets, the learning and memory ability of Long Short-Term Memory (LSTM) for time series, and the noise reduction and processing ability of wavelet basis for the signals to meet the requirement of the learning and classification of ECG signal characteristics. Sufficient experimental results show that the proposed model is feasible and effective in the electrocardiogram signal pattern classification.
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Feng et al. (2022) studied ECG signal classification. 13-layer deep dynamic neural network model (DDNN) was evaluated on ECG signal pattern classification. A novel 13-layer deep dynamic neural network model combining wavelet, convolutional, and recurrent blocks was feasible and effective for electrocardiogram signal pattern classification.
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