dorsal/arxiv
View SchemaHow 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.10191vv1 |
| URL | https://arxiv.org/abs/2601.10191 |
| 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.
{
"annotation_id": "c99baf0e-2546-4c80-a9f3-57a761fa0777",
"date_created": "2026-02-17T05:53:24.388000Z",
"date_modified": "2026-02-17T05:53:24.388000Z",
"file_hash": "cfcbeacc1818de8889a59f5da46f207d2ab886a8e382cbf4704c6b6b1f765a56",
"private": false,
"record": {
"abstract": "Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals\u0027 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.",
"arxiv_id": "2601.10191",
"authors": [
"Mathieu Cherpitel",
"Janne Luijten",
"Thomas B\u00e4ck",
"Camiel Verhamme",
"Martijn Tannemaat",
"Anna Kononova"
],
"categories": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series",
"url": "https://arxiv.org/abs/2601.10191",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "be583c24-751b-46ab-9f28-0964dfea7431",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}