论文标题
使用举重，对数定量的卷积神经网络检测心房颤动检测
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
论文作者
论文摘要
深神经网络（DNN）是医疗应用中有前途的工具。但是，由于通信的能源成本很高，因此在电池供电设备上实施复杂的DNN是具有挑战性的。在这项工作中，开发了一种卷积神经网络模型，用于检测心电图（ECG）信号的房颤。尽管接受了有限的可变长度输入数据训练，但该模型仍表现出高性能。重量修剪和对数定量合并以引入稀疏性并降低模型大小，可以利用这些稀疏性，以减少数据运动和降低计算复杂性。最终模型达到了91.1％的模型压缩比，同时保持高模型精度为91.7％，损失少于1％。
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.
