Task- and Metric-Specific Signal Quality Indices for Medical Time Series

TL;DR

This paper introduces a task- and metric-specific signal quality index (pSQI) for medical time series that effectively detects unreliable signals by modeling performance degradation under noise, outperforming existing SQIs without training.

Contribution

The paper formalizes signal quality as task- and metric-dependent and proposes a novel perturbation-based SQI that detects performance issues without requiring training.

Findings

pSQI outperforms existing SQIs in identifying unreliable signals.

pSQI does not require training, simplifying implementation.

Experiments on ECG and PPG benchmarks validate effectiveness.

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 requirements for task- and metric-specific SQIs, including monotonicity of the metric in expectation and maximal separation under thresholding. Experiments on R-peak detection and atrial fibrillation classification benchmarks demonstrate that the proposed pSQI consistently outperforms existing feature- and deep learning-based SQIs in identifying unreliable inputs without requiring training.