Myotonia, which refers to delayed muscle relaxation after contraction, is the
main symptom of myotonic dystrophy patients. We propose a hierarchical
attention-based temporal convolutional network (HA-TCN) for myotonic dystrohpy
diagnosis from handgrip time series data, and introduce mechanisms that enable
model explainability. We compare the performance of the HA-TCN model against
that of benchmark TCN models, LSTM models with and without attention
mechanisms, and SVM approaches with handcrafted features. In terms of
classification accuracy and F1 score, we found all deep learning models have
similar levels of performance, and they all outperform SVM. Further, the HA-TCN
model outperforms its TCN counterpart with regards to computational efficiency
regardless of network depth, and in terms of performance particularly when the
number of hidden layers is small. Lastly, HA-TCN models can consistently
identify relevant time series segments in the relaxation phase of the handgrip
time series, and exhibit increased robustness to noise when compared to
attention-based LSTM models.

本文介绍了一种层次化的基于注意力机制的时间卷积神经网络 (HA-TCN)，能够从手握时间序列数据中准确诊断肌无力症，并且具有模型可解释性等优点。在分类准确率和 F1 分数方面，我们发现所有深度学习模型的性能都相似，而它们都优于 SVM。此外，与网络深度无关，HA-TCN 模型在计算效率和性能方面都优于 TCN 模型，特别是在隐藏层数较少的情况下。最后，HA-TCN 模型可以一致地识别与松弛阶段有关的时间序列段，并且与基于注意力机制的 LSTM 模型相比具有更强的噪声鲁棒性。