How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series
Biomedical Signal Processing and Control, vol. 127, pp. 111061
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 and classification performance degradation showed that, on the EMGLAB dataset, decimation with anti-aliasing filtering better preserves signal characteristics key to the classification of neuromuscular diseases compared to shape-aware downsampling strategies when using the tsfresh feature set. 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.
Authors 6
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
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Leiden University · Leiden University Medical Center · Amsterdam University Medical Centers
Affiliation as printed
Amsterdam University Medical Centre, Department of Neurology, Amsterdam, The Netherlands
Leiden University Medical Centre, Department of Neurology, Leiden, The Netherlands
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
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Amsterdam University Medical Centers
Affiliation as printed
Amsterdam University Medical Centre, Department of Neurology, Amsterdam, The Netherlands
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Leiden University · Leiden University Medical Center
Affiliation as printed
Leiden University Medical Centre, Department of Neurology, Leiden, The Netherlands
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
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