X-RCRNet: An explainable deep-learning network for COVID-19 detection using ECG beat signals.

• An explainable Deep Neural Network to realize online monitoring of COVID-19 gravity by using ECG beat signal. • The network is based on ResNet18 with few enhancements: 1) Adding LSTM Layer inside the residual block, for regenerating the backpropagation error and further extracting the involved time-varying features; 2) Using LeakyReLU instead of ReLU for increasing the performances of the model. • The model outperformed the related existing methods in terms of accuracy and robustness. • The model originally identifies the ST interval of the ECG pattern, as the most prominent key features affected by the virus. Wearable systems measuring human physiological indicators with integrated sensors and supervised learning-based medical image analysis (e.g. ECG, X-ray, CT or ultrasound images for lung or the chest) have been considered relevant tools for COVID-19 monitoring and diagnosis. However, these two technical roadmaps have their respective advantages and drawbacks. The current wearable systems enable to realize real-time monitoring of COVID-19 but are limited to its basic symptoms only, neither allowing to distinguish it from other diseases nor performing deep analysis. Current medical image analysis can provide accurate decision support for diagnosis but rarely deals with real-time data processing. In this context, we propose a new wearable system by combining the advantages of these two technical roadmaps. Considering that electrocardiogram (ECG) has been proved relevant to evolution of COVID-19 symptoms, the proposed wearable system will integrate an explainable Deep Neural Network to realize online monitoring of COVID-19 gravity by using ECG beat signal. This paper will focus on the Deep Neural Network model named X-RCRNet. The network is based on ResNet18 but with few enhancements: 1) LSTM Layers for regenerating the backpropagation error and further extracting the involved time-varying features; 2) LeakyReLU for increasing the performances of the model. With an accuracy of 96.48 % after experiments, our model has not only outperformed the existing methods in terms of accuracy and robustness, but also originally identify the ST interval of the ECG pattern, as the most prominent key features affected by the virus. [ABSTRACT FROM AUTHOR]