כתבה
arXiv cs.AI ·
השפעת Downsampling על אותות nEMG
How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series
חוקרים בדקו את השפעת Downsampling על אותות nEMG. הם פיתחו תהליך עבודה להערכת הפסדי מידע עקב Downsampling. התוצאות מראות כי אלגוריתמים מודעים לצורה משפרים את שימור המידע האבחוני.
תקציר מקורי באנגליתarXiv:2601.10191v2 Announce Type: replace 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 f
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arxiv.org
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