发表机构
National Taiwan University; National Yang Ming Chiao Tung University; Academia Sinica; Chung Yuan Christian University（台湾大学; 国立阳明交通大学; 中央研究院; 中原大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出可解释自适应的Prism-SQA框架，通过生理感知源分离与验证分解sEMG污染，实现透明质量评估，性能优于黑箱方法。
AI 中文摘要
表面肌电信号（sEMG）易受各种污染物影响，这些污染物会扭曲信号形态和频谱内容。准确的信质量评估（SQA）对于识别此类退化并确保可靠的临床分析和决策至关重要。近年来，基于神经网络的信质量评估方法通过学习复杂的污染模式实现了准确的质量估计，但其黑箱特性阻碍了临床医生理解或验证所报告的分数，并限制了其在不重新训练的情况下适应特定应用的质量定义的能力。为解决这些局限性，我们提出了Prism-SQA，一种可解释且自适应的神经框架，它将信质量评估重新构建为一种生理感知的信源分离与验证过程。Prism-SQA使用带有双向长短期记忆网络的U-Net将每个输入信号分解为一个干净的sEMG分量和五个特定污染物分量。每个分离的污染物分量由污染物指纹验证器检查，该验证器通过将其时域和频域结构与典型污染物特征进行比较来强制生理合理性。这种设计使临床医生能够检查每种污染物如何影响信号质量，将评估建立在透明的、信号级证据之上，而非不透明的潜在表示。基于已验证分量计算的质量指数进一步支持在不重新训练的情况下跨临床环境定制质量标准。我们使用来自公共Ninapro数据集的合成噪声sEMG对Prism-SQA进行连续质量评分估计评估，并使用临床吞咽困难数据集进行二元质量分类评估。结果表明，Prism-SQA在提供明确可解释性和适应性的同时，实现了与当代黑箱神经方法相当或更优的性能，推动了实用且临床一致的sEMG信质量评估的发展。
英文摘要
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.
Comments14 pages, 9 figures, 7 tables, accepted by IEEE Journal of Biomedical and Health Informatics (JBHI)
