Abstract
A lightweight deep learning algorithm called LTE Network was devised to automatically detect arrhythmias from original electrocardiograms (ECG) with small model size without sacrificing noticeable accuracy. The algorithm is based on a cascaded architecture that uses point-depthwise convolutions, which combine a pointwise convolution with a depthwise convolution to build a nine-layer lightweight convolutional neural network. Furthermore, we use an optimized loss function and Adam optimizer which minimize classification errors and alleviate vanishing gradient problem in the learning process. The experiments are conducted in original datasets of ECG signals coming from MIT-BIH ECG databases. It is contrasted with AlexNet and MobileNet, and the results confirm that the LTE Network outperform others on accuracy and efficiency.
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Acknowledgements
The project is supported by the Program of Scientific and Technological Research of Henan Province, China (152102210249) and National Natural Science Foundation of China (61602422) and Natural Science Foundation of Henan Province (152300410047) and Foundation of Henan Educational Committee (18A520049).
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Li, R., Zhang, X., He, Z., Shi, D., Zhao, H., Liu, W. (2019). A Lightweight Neural Network to Detect Arrhythmias. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_14
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