Sliding large kernel of deep learning algorithm for mobile electrocardiogram diagnosis. (December 2021) - Tseng, Kuo-Kun; Wang, Chao; Xiao, Tianjie; Chen, Chien-Ming; Hassan, Mohammad Mehedi; de Albuquerque, Victor Hugo C.
Highlights: A new diagnostic framework for mobile electrocardiogram signal is proposed. The sliding segmentation method is used to enhance the model's generalization ability. Adopted a large-scale convolution kernel of a one-dimensional neural network. The proposed algorithm shows more resistant to sparseness and noise issues. Experimental results present better accuracy as compared to previous approaches. Abstract: Cardiovascular disease has become a significant cause of modern people's health problems. The mobile electrocardiogram can be a non-intrusive and convenient tool to provide an auxiliary diagnosis of cardiovascular diseases for the virtual heath community. In this paper, a new diagnostic framework for mobile electrocardiogram signal is proposed with two improved methods. They are (1) the sliding segmentation method, which enhances the model's generalization ability and (2) the large-scale convolution kernel of a one-dimensional neural network, that is designed for mobile electrocardiogram signal with more resistant to sparseness and noise issues. According our experiments, the results show that the proposed sliding large kernel algorithm has a better accuracy than previous algorithms. Graphical abstract: Image, graphical abstract Sliding large kernel of deep neural network for mobile electrocardiogram diagnosis of the virtual heath community.