How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series
This provides practical guidance for enabling near real-time nEMG analysis in clinical settings, though it is incremental as it applies existing methods to a specific domain problem.
The study tackled the problem of high computational load in automated needle electromyography (nEMG) analysis for neuromuscular disease detection by evaluating how downsampling affects signal integrity and classification performance, finding that shape-aware algorithms outperform standard decimation in preserving diagnostic information while reducing computational load.
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.