Problem
Research questions and friction points this paper is trying to address.
sEMG
signal quality assessment
contamination
neural network
black-box
Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment
🤖 AI Summary
为解决sEMG信号质量评估的可解释性和适应性问题，提出Prism-SQA框架，通过生理感知源分离和验证过程，实现信号分解与质量定制。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
interpretable and adaptable neural framework
physiology-aware source-separation
contaminant fingerprint verifier
transparent signal-level evidence
customization of quality criteria
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