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Signal Quality Index (SQI) in PPG Analysis
A Signal Quality Index (SQI) is a numerical metric that automatically quantifies the reliability of a PPG segment for physiological analysis. SQIs typically range from 0 (unacceptable) to 1 (clean signal) and are used to gate downstream algorithms — only high-quality segments are processed for SpO2, HRV, or other metrics.
Multiple SQI approaches have been developed, each targeting different artifact manifestations. Template-based SQI methods (e.g., Li and Clifford, 2012, Physiol Meas, doi:10.1088/0967-3334/33/9/1491) compare each detected beat with a reference template derived from the same recording; low correlation flags beats as potentially corrupted. Spectral SQI methods compare the power spectrum of the PPG segment with an idealized cardiac spectrum, penalizing excess power outside the expected heart rate harmonics. Morphological SQI methods check physiological plausibility of waveform features (amplitude, width, diastolic notch presence).
The PhysioNet Computing in Cardiology Challenge 2014 spurred extensive development of combined SQI frameworks.
In clinical monitoring, SQI thresholds directly impact alarm fatigue. Consumer wearables typically use simpler SQI proxies — signal standard deviation, autocorrelation peak height, or accelerometer-based motion detection — to trigger measurement retry rather than continuous quality monitoring.
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Frequently Asked Questions
What SQI threshold should be used before HRV analysis?
There is no universal threshold. Choose a cut-off validated for your SQI method and data, and report it alongside the HRV results.
Can machine learning improve SQI accuracy?
Yes.
How does SQI differ from signal-to-noise ratio (SNR)?
SNR is a general measure of signal clarity relative to noise, typically computed in the frequency domain. SQI is a clinically-oriented composite metric that assesses physiological plausibility, not just spectral purity.