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    A knowledge-driven graph convolutional network for abnormal electrocardiogram diagnosis
    (Elsevier, 2024) Ge, Zhaoyang; Cheng, Huiqing; Tong, Zhuang; He, Ziyang; Alhudhaif, Adi; Polat, Kemal; Xu, Mingliang
    The electrocardiogram (ECG) signal comprising P-, Q-, R-, S-, and T -waves is an indispensable noninvasive diagnostic tool for analyzing physiological conditions of the heart. In general, traditional ECG intelligent diagnosis methods gradually extract features of the signal from input data until they can classify the ECG signal. However, the decision -making process of ECG intelligence models is implicit to clinicians. Clinical experts rely on clear and specific features extracted from ECG data to diagnose cardiac diseases effectively. Inspired by this clinical diagnosis mechanism, we propose an ECG knowledge graph (ECG -KG) framework primarily to improve ECG classification by presenting knowledge of ECG clinical diagnosis. In particular, the ECG -KG framework contains an ECG semantic feature extraction module, a knowledge graph construction module, and an ECG classification module. First, the ECG semantic feature extraction module locates the key points using the difference value method and further calculates the ECG attribute features. Further, the knowledge graph construction module utilizes attribute features to design entities and relationships for constructing abnormal ECG triples. The triples vectorize ECG abnormalities through the strategy of knowledge graph embedding strategy. Finally, the ECG classification module combines the ECG knowledge graph with the graph convolutional network model and adequately integrates expert knowledge to identify ECG abnormalities. Experiments conducted on the benchmark QT, the CPSC-2018, and the ZZU-ECG datasets show that the ECG -KG framework considerably outperforms other ECG diagnosis models, indicating the effectiveness of the ECG -KG framework for ECG abnormality diagnosis.
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    A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification
    (Pergamon-Elsevier Science Ltd, 2023) He, Ziyang; Chen, Yufei; Yuan, Shuaiying; Zhao, Jianhui; Yuan, Zhiyong; Polat, Kemal
    Electrocardiogram (ECG) is an effective non-invasive tool that can detect arrhythmias. Recently, deep learning (DL) has been widely used in ECG classification algorithms. However, differences between subjects lead to data shifts, hindering the further extension of DL algorithms. To solve this problem, we propose a novel multi-level unsupervised domain adaptation framework (MLUDAF) to diagnose arrhythmias. During feature extraction, we use the atrous spatial pyramid pooling residual (ASPP-R) module to extract spatio-temporal features of the samples. Then the graph convolutional network (GCN) module is used to extract the data structure features. During domain adaptation, we design three alignment mechanisms: domain alignment, semantic alignment, and structure alignment. The three alignment strategies are integrated into a unified deep network to guide the feature extractor to extract domain sharing and distinguishable semantic representations, which can reduce the differences between the source and target domains. Experimental results based on the MIT-BIH database show that the proposed method achieves an overall accuracy of 96.8% for arrhythmia detection. Compared to other methods, the proposed method achieves competitive performance. Cross-domain experiments between databases also demonstrate its strong generalizability. Therefore, the proposed method is promising for application in medical diagnosis systems.