Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network.
Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network.
复制标题
利用深度神经网络预测兔子力竭游泳运动性猝死
DOI：
10.1186/s12938-021-00925-0
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发表时间：
2021-08-30
影响因子：
3.9
通讯作者：
Guo X
中科院分区：
文献类型：
--
作者：
Zhang Y;Zheng Y;Wang M;Guo X
背景与目的适度的运动有助于身体健康。但过度运动可能导致心脏疲劳、心肌损伤甚至运动性猝死。心脏健康监测对预防运动性猝死具有重要意义。心电图、超声心动图、血压和组织学分析等诊断方法均表明，心律失常和左室纤维化是运动性猝死的预警症状。心音可反映心脏瓣膜、心脏血流和心肌功能的变化。深度学习因其识别疾病的能力而引起广泛关注。因此，提出了一种结合HS的深度学习方法来预测新西兰兔的运动性猝死。目的是建立一种预测新西兰兔运动性猝死的方法。方法提出了一种基于卷积神经网络（CNN）和门控递归单元（GRU）的新西兰兔运动性猝死预测方法。采用负重力竭游泳实验，测定运动性猝死和存活新西兰兔（n= 11/10）在4个不同时间点的HS。然后采用改进的Viola积分法和双阈值法对HS信号进行分割。将不同时间点的分割HS帧作为CNN和GRU组合的CNN-GRU网络的输入，以完成对运动猝死的预测。以第四个时间点分割的HS帧为输入，结果表明，该网络具有更好的性能，准确率为89.57%，灵敏度为89.38%，特异性为92.20%。此外，将不同时间点的分割HS帧输入CNN-GRU网络，结果显示，随着实验的进行，新西兰兔运动性猝死的预测准确率从50.98%提高到89.57%。结论所提出的网络在HS分类方面表现出良好的性能，证明了深度学习探索运动性猝死的可行性。此外，它可能在帮助人类探索运动猝死方面具有重要意义。
Background and objectiveModerate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits.MethodsThis paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n= 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN–GRU network to complete the prediction of exercise sudden death.ResultsIn order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN–GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%.ConclusionThe proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death.
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