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[2602.12478] Task- and Metric-Specific Signal Quality Indices for Medical Time Series
Summary
The paper introduces a new perturbation-based signal quality index (pSQI) for medical time series, addressing the limitations of existing task-agnostic indices in detecting unreliable signals for clinical algorithms.
Why It Matters
As automated algorithms increasingly inform clinical decisions, ensuring the reliability of medical time series data is crucial. This research provides a framework for evaluating signal quality that is tailored to specific tasks and metrics, potentially improving patient outcomes by enhancing the accuracy of medical signal analysis.
Key Takeaways
- Existing signal quality indices (SQIs) are often task-agnostic, limiting their effectiveness.
- The proposed perturbation-based SQI (pSQI) detects performance degradation in algorithms analyzing medical signals.
- Formal requirements for task- and metric-specific SQIs are established, enhancing their applicability.
- Experiments show that pSQI outperforms traditional feature-based and deep learning SQIs.
- Improving signal quality assessment can lead to better clinical decision-making.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.12478 (eess) [Submitted on 12 Feb 2026] Title:Task- and Metric-Specific Signal Quality Indices for Medical Time Series Authors:Jad Haidamous, Christoph Hoog Antink View a PDF of the paper titled Task- and Metric-Specific Signal Quality Indices for Medical Time Series, by Jad Haidamous and 1 other authors View PDF HTML (experimental) Abstract:Medical time series such as electrocardiograms (ECGs) and photoplethysmograms (PPGs) are frequently affected by measurement artifacts due to challenging acquisition environments, such as in ambulances and during routine daily activities. Since automated algorithms for analyzing such signals increasingly inform clinically relevant decisions, identifying signal segments on which these algorithms may produce unreliable outputs is of critical importance. Signal quality indices (SQIs) are commonly used for this purpose. However, most existing SQIs are task agnostic and do not account for the specific algorithm and performance metric used downstream. In this work, we formalize signal quality as a task- and metric-dependent concept and propose a perturbation-based SQI (pSQI) that aims to detect an algorithm's performance degradation on an input signal with respect to a metric. The pSQI is defined as the worst-case value of the performance metric under an additive, colored Gaussian noise perturbation with a lower-bounded signal-to-noise ratio. We introduce formal require...
