A Novel Multi-Resolution Heart Rate Variability Analysis for IoT-based Drowsiness Detection: Preserving Temporal Trends in Features Series

Heart rate variability (HRV) analysis provides valuable insights into autonomic nervous system regulation and serves as an effective indicator for drowsiness detection in transportation safety applications. This study presents the so-called Multi-CNM, a novel approach to HRV analysis for drowsiness detection through multi-resolution extraction of HRV features without additional processing to preserve the temporal trends in its series. To determine if our approach effectively captures drowsiness-related patterns in HRV features series, we compare it with two commonly used techniques in signal windowing and feature extraction: 1) the standard single window analysis with overlapping, redefined as CNM, 2) the well-known multi-scale entropy (MSE), used to measure the complexity of the time series and therefore give insights to abnormal events, applied on top of our approach (Multi-CNM-MSE). Experimental evaluations on a public drowsiness detection dataset demonstrate that our proposed Multi-CNM approach shows promising results achieving up to 99% accuracy and F1-score, comparable to the standard CNM and the coarse-grained Multi-CNM-MSE techniques, with up to 93% and 95% respectively. Statistical analysis reveals that the time series of HRV features such as MeanNN, LFHF ratio, and pNN20 exhibit the strongest discriminative power between drowsy and non-drowsy states, significantly underlined by our Multi-CNM. Our findings highlight the importance of preserving multi-resolution temporal structure in HRV analysis for real-time drowsiness detection in resource-constrained IoT implementations. The proposed methodology balances computational efficiency with detection accuracy, making it suitable for deployment in wearable and automotive monitoring systems.