计算机科学
人工智能
深度学习
信号(编程语言)
模式识别(心理学)
程序设计语言
作者
Ananya Mantravadi,Siddharth Saini,Sai Chandra Teja R.,Sparsh Mittal,Syed Muhammad Hassan Shah,R. Sri Devi,Rekha Singhal
标识
DOI:10.1016/j.jelectrocard.2024.01.004
摘要
Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this paper, we propose a deep learning network CLINet for ECG signal classification. Our network uses convolution, LSTM and involution layers to bring their unique advantages together. For both convolution and involution layers, we use multiple, large size kernels for multi-scale representation learning. CLINet does not require complicated pre-processing and can handle electrocardiograms of any length. Our network achieves 99.90% accuracy on the ICCAD dataset and 99.94% accuracy on the MIT-BIH dataset. With only 297 K parameters, our model can be easily embedded in smart wearable devices.
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