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

• URL: http://arxiv.org/abs/2601.10191v1
• Date: Thu, 15 Jan 2026 08:46:56 GMT
• Title: How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series
• Authors: Mathieu Cherpitel, Janne Luijten, Thomas Bäck, Camiel Verhamme, Martijn Tannemaat, Anna Kononova,
• Abstract summary: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases.<n>Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood.<n>This study presents a workflow for systematically evaluating information loss caused by downsampling in high-frequency time series.
• Score: 1.3648865252191944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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