Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks

• URL: http://arxiv.org/abs/2206.07649v1
• Date: Tue, 14 Jun 2022 11:47:04 GMT
• Title: Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
• Authors: Xiu Qi Chang, Ann Feng Chew, Benjamin Chen Ming Choong, Shuhui Wang, Rui Han, Wang He, Li Xiaolin, Rajesh C. Panicker, Deepu John
• Abstract summary: A convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram signals. The model demonstrates high performance despite being trained on limited, variable-length input data. The final model achieved a 91.1% model compression ratio while maintaining high model accuracy of 91.7% and less than 1% loss.
• Score: 25.160063477248904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In this work, a convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram (ECG) signals. The model demonstrates high performance despite being trained on limited, variable-length input data. Weight pruning and logarithmic quantisation are combined to introduce sparsity and reduce model size, which can be exploited for reduced data movement and lower computational complexity. The final model achieved a 91.1% model compression ratio while maintaining high model accuracy of 91.7% and less than 1% loss.

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