Combining A CNN Auto-Encoder with An MLP To Reduce the Computational Cost of Heart Arrhythmia Detection from ECG
Keywords:
ECG signal, CNN, Deep learning, auto-encoder, arrhythmia, MLP.Abstract
Heart disease is one of the leading causes of death worldwide, and cardiac arrhythmia is a major symptom of heart disease. Due to the inherent differences between raw time series data and classical features, it is not possible to take advantage of models such as convolutional neural network (CNN), which have a high ability to analyze ECG data. To overcome this limitation, this paper proposes a new idea for ECG classification. The proposed idea is to use separate inputs for raw data and classical features. Based on the proposed idea, two different models of the deep neural network are presented. A more complex architecture consists of a CNN with a Residual structure and an MLP. The second architecture is proposed by combining a CNN auto-encoder with an MLP to reduce the computational cost and the possibility of implementing the proposed idea in wearable devices that are limited in terms of processing capacity and energy consumption. Also, to approach the imbalance problem of existing ECG databases, new approaches have been proposed for oversample and undersample methods. Experimental results on the standard MIT-BIH Arrhythmia database showed a 14.73% increase in recall criterion by the first architecture and 1.27% by the second architecture, compared to the conventional architecture in the intra-patient paradigm. In-Addition, the proposed oversampling and undersampling methods increased the first architectural recall for the VEB+ class by 6.98% and 2.33%, respectively in the intra-patient paradigm.
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