Programa del congreso

Heart Rate Variability (HRV) analysis from photoplethysmography (PPG) signals requires robust signal quality assessment to ensure accurate Pulse Rate Variability (PRV) measurements. This study introduces an enhanced Signal Quality Index (SQI) analysis incorporating five quality measuring metrics: kurtosis, skewness, entropy, perfusion index, and mean crossing rate. A dynamic quantile-based normalization method (5th-95th percentiles) standardizes SQI values across diverse physiological conditions.

Methods: PPG signals from 235 subjects (225 sessions) were processed using hierarchical segmentation (10-second to 5-minute windows) at 250Hz sampling rate. Five SQI metrics were computed at 10Hz filtering with dynamic normalization. Correlation analyses assessed relationships between SQI metrics and RMSSD errors compared to reference ECG measurements.

Results: Analysis of 1,825 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=-0.226, R²=5.1%), followed by Skewness (r=-0.152, R²=2.3%), Entropy (r=-0.148, R²=2.2%), Kurtosis (r=-0.109, R²=1.2%), and Mean Crossing Rate (r=-0.077, R²=0.6%). Quality distribution analysis showed 83.8% of segments exceeded 80% quality for Perfusion Index, while Mean Crossing Rate provided complementary temporal stability assessment.

Conclusions: The enhanced multi-metric analysis with dynamic normalization provides robust PPG signal quality assessment. Perfusion Index offers the most reliable quality indicator, while Mean Crossing Rate contributes valuable temporal regularity information for comprehensive quality evaluation in automated PPG-based HRV monitoring systems.