KFG-MLNet: an ECG-based deep network using knowledge-guided features and multitask learning for arrhythmias classification Online publication date: Thu, 23-May-2024
by Yanyun Tao; Yuzhen Zhang
International Journal of System Control and Information Processing (IJSCIP), Vol. 4, No. 2, 2024
Abstract: Deep learning networks performed well in electrocardiogram (ECG)-based arrhythmia classification. However, the classification accuracy and the requirement of lots of training samples are always the issues. In this study, we proposed a model called KFG-MLNet, which integrated expert knowledge-guided feature representation with two lightweight convolutional neural networks (CNNs). KFG-MLNet used a feature representation based on expert knowledge to better express the rhythm and heartbeat shape. This representation fully utilised the inequality of the R-R interval in an abnormal rhythm and the correlation between interheartbeats. Then, KFG-MLNet employed CNNs to realise rhythm and heartbeat arrhythmia classification, respectively. Multitask learning was used to train two CNNs on rhythm and heartbeat arrhythmia classification, and improve each other by sharing the convolutional layer. On the dataset of MIT-BIH, KFG-MLNet improved the accuracy by 2.07%, 10.05% and 3.68% over the other models.
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