目的:神经肌肉疾病是一种损害我们控制身体运动能力的疾病。针状肌电图(nEMG)通常用于诊断神经肌肉疾病,这是一种电生理测试,使用侵入性针头测量肌肉产生的电信号。nEMG信号的特征由肌电图医师手动分析,以诊断神经肌肉疾病的类型,这个过程高度依赖于肌电图师的主观经验。当代计算机辅助方法利用深度学习图像分类模型来对未针对信号分类进行优化的nEMG信号进行分类。此外,模型的可解释性没有得到解决,这在医学应用中至关重要。本研究旨在提高预测精度,推理时间,并解释nEMG神经肌肉疾病分类中的模型预测。
方法:本研究介绍了nEMGNet,一种具有残差连接的一维卷积神经网络,旨在从原始信号中提取特征,与以前的图像分类模型相比,具有更高的精度和更快的速度。接下来,分裂投票(DiVote)算法旨在整合每个受试者的异质nEMG信号数据结构,并利用肌肉亚型信息获得更高的准确性。最后,特征可视化用于识别nEMGNet诊断预测的因果关系,为了确保nEMGNet对有效功能进行预测,不是文物。
结果:使用2015年6月至2020年7月在首尔国立大学医院从57名受试者测量的376个nEMG信号对所提出的方法进行了测试。三级分类任务的结果表明,nEMGNet对nEMG信号段的预测精度为62.35%,nEMGNet和DiVote算法的主题诊断预测准确率为83.69%,超过5倍交叉验证。nEMGNet优于以前在nEMG诊断分类方面的所有模型,和特征可视化结果的启发式分析表明,nEMGNet学习了相关的nEMG信号特征。
结论:这项研究引入了nEMGNet和DiVote算法,该算法在基于nEMG信号预测神经肌肉疾病方面表现出快速准确的性能。所提出的方法可以应用于医学以支持实时电生理诊断。
OBJECTIVE: Neuromuscular disorders are diseases that damage our ability to control body movements. Needle electromyography (nEMG) is often used to diagnose neuromuscular disorders, which is an electrophysiological test measuring electric signals generated from a muscle using an invasive needle. Characteristics of nEMG signals are manually analyzed by an electromyographer to diagnose the types of neuromuscular disorders, and this process is highly dependent on the subjective experience of the electromyographer. Contemporary computer-aided methods utilized deep learning image classification models to classify nEMG signals which are not optimized for classifying signals. Additionally, model explainability was not addressed which is crucial in medical applications. This study aims to improve prediction accuracy, inference time, and explain model predictions in nEMG neuromuscular disorder classification.
METHODS: This study introduces the nEMGNet, a one-dimensional convolutional neural network with residual connections designed to extract features from raw signals with higher accuracy and faster speed compared to image classification models from previous works. Next, the divide-and-vote (DiVote) algorithm was designed to integrate each subject\'s heterogeneous nEMG signal data structures and to utilize muscle subtype information for higher accuracy. Finally, feature visualization was used to identify the causality of nEMGNet diagnosis predictions, to ensure that nEMGNet made predictions on valid features, not artifacts.
RESULTS: The proposed method was tested using 376 nEMG signals measured from 57 subjects between June 2015 to July 2020 in Seoul National University Hospital. The results from the three-class classification task demonstrated that nEMGNet\'s prediction accuracy of nEMG signal segments was 62.35%, and the subject diagnosis prediction accuracy of nEMGNet and the DiVote algorithm was 83.69 %, over 5-fold cross-validation. nEMGNet outperformed all models from previous works on nEMG diagnosis classification, and heuristic analysis of feature visualization results indicate that nEMGNet learned relevant nEMG signal characteristics.
CONCLUSIONS: This study introduced nEMGNet and DiVote algorithm which demonstrated fast and accurate performance in predicting neuromuscular disorders based on nEMG signals. The proposed method may be applied in medicine to support real-time electrophysiologic diagnosis.