Advancing interpretable cardiac disease diagnosis via a transformer-convolutional hybrid network on electrocardiograms

Manual heart disease diagnosis with the electrocardiogram (ECG) is intractable due to the intertwined signal features and lengthy diagnosis procedure, especially for the 24-hour dynamic ECG signals. Consequently, even experienced cardiologists may face difficulty in producing all accurate ECG reports. In recent years, Artificial Intelligence (AI), particularly neural network-based automatic ECG diagnosis methods have exhibited promising performance, suggesting a potential alternative to the labor-intensive examination conducted by cardiologists. However, many existing approaches failed to adequately consider the temporal and channel dimensions when assembling features and ignored interpretability. And clinical theory underscores the necessity of prolonged signal observations for diagnosing certain ECG conditions such as tachycardia. Moreover, specific heart diseases manifest primarily through distinct ECG leads represented as channels. In response to these challenges, this paper introduces a novel neural network architecture for ECG classification (diagnosis). The proposed model incorporates Lead Fusing blocks, transformer-XL (meaning extra long) encoder-based Encoder modules, and hierarchical temporal attentions. Importantly, this classifier operates directly on raw ECG time-series signals rather than cardiac cycles. Signal integration begins with the Lead Fusing blocks, followed by the Encoder modules and hierarchical temporal attentions, enabling the extraction of long-dependent features. Furthermore, existing convolution-based methods have been argued to compromise interpretability, whereas the proposed neural network provides improved clarity in this regard. Experimental evaluations on a comprehensive public dataset confirm the superiority of the proposed classifier over state-of-the-art methods. Moreover, a visualization method was employed to generate a location map that demonstrates the areas of the signal emphasized by the model, thereby enhancing interpretability. • Our model extracts long-dependent features of ECG signals based on the Transformer-XL encoder. • The proposed network offers the improved interpretability. • Our classifier achieves superior performance over other state-of-the-art methods.

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