# How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

arXiv: https://arxiv.org/abs/2601.10191 · HTML: https://mlsanity.com/paper/2601.10191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Geometric feature performance under downsampling for EEG classification tasks](https://mlsanity.com/paper/2102.07669/) (2021)
- [MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB](https://mlsanity.com/paper/2305.00332/) (2023)
- [tsdownsample: high-performance time series downsampling for scalable visualization](https://mlsanity.com/paper/2307.05389/) (2023)
- [Exploring Best Practices for ECG Pre-Processing in Machine Learning](https://mlsanity.com/paper/2311.04229/) (2023)

## Abstract (arXiv)

Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis. Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood. This study presents a workflow for systematically evaluating information loss caused by downsampling in high-frequency time series. The workflow combines shape-based distortion metrics with classification outcomes from available feature-based machine learning models and feature space analysis to quantify how different downsampling algorithms and factors affect both waveform integrity and predictive performance. We use a three-class NMD classification task to experimentally evaluate the workflow. We demonstrate how the workflow identifies downsampling configurations that preserve diagnostic information while substantially reducing computational load. Analysis of shape-based distortion metrics showed that shape-aware downsampling algorithms outperform standard decimation, as they better preserve peak structure and overall signal morphology. The results provide practical guidance for selecting downsampling configurations that enable near real-time nEMG analysis and highlight a generalisable workflow that can be used to balance data reduction with model performance in other high-frequency time-series applications as well.