  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.
