🤖 AI Summary
This study addresses the challenge of high computational cost and poor real-time performance in automated classification of neural muscular disorders using high-sampling-rate needle electromyography (nEMG) signals, where the impact of downsampling on diagnostic information retention remains unclear. The authors propose a unified evaluation framework that, for the first time, integrates shape-aware downsampling, feature space analysis, and classification performance to systematically quantify how different downsampling strategies affect waveform integrity and diagnostic capability in high-frequency temporal biosignals. Evaluated on a three-class neuromuscular disorder classification task, the framework identifies an optimal downsampling configuration that balances computational efficiency with preservation of diagnostic information. Results demonstrate that shape-aware downsampling significantly outperforms conventional methods, offering a generalizable analytical paradigm for efficient processing of high-frequency temporal biosignals.
📝 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.