# Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis

arXiv: https://arxiv.org/abs/1903.11748 · HTML: https://mlsanity.com/paper/1903.11748/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 10 of 10)

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- [TCNFormer: Temporal Convolutional Network Former for Short-Term Wind Speed Forecasting](https://mlsanity.com/paper/2408.15737/) (2024)
- [Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification](https://mlsanity.com/paper/2506.00337/) (2025)
- [Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data](https://mlsanity.com/paper/2506.07092/) (2025)
- [EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming](https://mlsanity.com/paper/2604.25499/) (2026)

## Abstract (arXiv)

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.