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

TL;DR

This paper introduces a workflow to evaluate how downsampling high-frequency needle electromyography signals affects information retention and classification accuracy, aiding real-time neuromuscular disease diagnosis.

Contribution

It presents a systematic method combining shape metrics and machine learning outcomes to optimize downsampling strategies for high-frequency time series analysis.

Findings

Shape-aware downsampling preserves waveform morphology better.

Optimal downsampling reduces computational load without sacrificing diagnostic accuracy.

Workflow is generalisable to other high-frequency time series applications.

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.