{
  "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": "402ececc-c8ea-4154-bcc0-1143c28f6cb4",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}