Heart Rate Variability (HRV) is a valuable marker of autonomic nervous system activity, commonly derived from electrocardiogram (ECG) recordings. Pulse Rate Variability (PRV), extracted from photoplethysmography (PPG), offers a convenient alternative, particularly in wearable health technologies. However, PRV accuracy depends heavily on PPG signal quality, which may be degraded by noise, motion artifacts, or poor sensor contact. This study analyzes the relationship between the quality of the PPG signal, measured with different SQIs, and its relationship with the error of HRV variability (specifically only the RMSSD) using multiple Signal Quality Indices (SQIs).
Methods: PPG signals from 225 sessions from 120 subjects were segmented into 10-second windows, then aggregated into 5-minute segments (n=1046). Four SQI metrics were computed: kurtosis, skewness, entropy, and perfusion index. A novel dynamic quantile-based normalization method standardized SQI values (0-1 scale). Correlation analyses assessed relationships between SQI metrics and RMSSD errors compared to reference ECG measurements.
Results: Analysis of 1,046 five-minute segments revealed significant negative correlations between all SQI metrics and HRV errors (p < 0.001). The Perfusion Index demonstrated the strongest correlation (r = -0.189, R² = 3.6%) and maintained predictive validity in high-quality segments (SQI > 80%, r = -0.151, p < 0.001), while other metrics lost discriminative power. Quality distribution varied considerably: 84.9% of segments exceeded 80% quality for Perfusion Index, compared to 33.7% for Skewness.
Conclusions: Perfusion Index provides the most robust PPG signal quality assessment across all quality levels, supporting automated signal acceptance for reliable HRV monitoring.