Neural system separates contaminants to score muscle signal quality
Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment
Machine Learning
Summary
Surface electromyography (sEMG) signals used in medical tests can get messed up by different types of noise, making analysis tricky. The authors developed Prism-SQA, a new method that breaks down these signals to pull out clean parts from several kinds of noise, showing exactly how each noise type affects quality. This helps doctors understand the quality ratings instead of relying on unclear black-box models, and lets them adjust quality rules without retraining. Their tests showed Prism-SQA works as well or better than existing methods while offering clearer explanations.
What this means in practice
- •For clinical device makers: Build muscle signal monitoring devices that transparently show noise impact on signal quality for easier clinical validation and customization.$Commercial implications: Enables clearer and adaptable sEMG quality feedback in medical devices, improving user trust and regulatory acceptance.
- •For biomedical engineers: Develop sEMG analysis tools that decompose signals into noise sources, allowing targeted noise reduction and quality interpretation without retraining.
Authors
Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao
Abstract
sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying such degradation and ensuring reliable clinical analyses and decisions. Recent neural network-based SQA methods achieve accurate quality estimation by learning complex contamination patterns, yet their black-box nature prevents clinicians from understanding or validating the reported scores and limits adaptability to application-specific quality definitions without retraining. To address these limitations, we propose Prism-SQA, an interpretable and adaptable neural framework that reformulates SQA as a physiology-aware source-separation and verification process. Prism-SQA decomposes each input signal into a clean sEMG component and five contaminant-specific components using a U-Net with bidirectional long short-term memory. Each separated contaminant component is examined by a Contaminant Fingerprint Verifier, which enforces physiological plausibility by comparing its temporal and spectral structure with canonical contaminant signatures. This design allows clinicians to inspect how each contaminant affects signal quality, grounding the assessment in transparent, signal-level evidence rather than opaque latent representations. Quality indices computed from the verified components further enable customization of quality criteria across clinical contexts without retraining. We evaluate Prism-SQA on continuous quality-score estimation using synthesized noisy sEMG from public Ninapro datasets and on binary quality classification using a clinical dysphagia dataset. Results show that Prism-SQA achieves competitive or better performance than contemporary black-box neural methods while providing explicit interpretability and adaptability, advancing toward practical and clinically aligned sEMG SQA.