Arousal detection by using ultra-short-term heart rate variability (HRV) analysis - Mohammadpoor Faskhodi, Mahtab; Fernández-Chimeno, Mireya; García-González, Miquel Angel (Frontiers in Medical Engineering, 2023)
The study of arousal is crucial as it helps to understand the role of increased physiological and psychological activation in emotions, motivation, cognitive performance, stress responses, sleep-wake cycles, and clinical application. Recent studies have shown that arousal is commonly associated with physiological changes including heart rate variability (HRV) as indicated by RR intervals. In some applications, the analysis requires analyzing short segments of RR time series, shorter than the usual 5 min HRV. The objective of this study is to check the performance of ultra-short-term HRV indices to track changes in arousal. Method: In this study, to follow arousal changes, 31 healthy subjects were examined in both non-arousal (relaxed) and aroused states. Two states of five minutes each are used to measure the relaxed and arousal states. After data collection, RR time series segments were obtained randomly for each subject in arousal and relaxed states in the 30s, 60s, 120s, and 240s time windows. Next, 17 ultra-short-term HRV indices were computed for each time window for RR intervals in relaxed and aroused states. Results and Discussion: Due to the findings, novel indices such as ACI and fnQ may aid in the recognition of arousal from relaxed status. The odds of ACI being higher for the same subject during a randomly selected arousal interval than during a randomly selected relax interval are 78%, 79%, 84%, and 89% for the 30s, 60s, 120s, and 240s time windows respectively. Similarly, the odds of fnQ being higher during arousal than during a relaxed state are 79%, 81%, 84%, and 85% for the 30s, 60s, 120s, and 240s time windows respectively. Therefore, ACI and fnQ provided the best performances in intra-individual arousal detection by using ultra-short-term HRV analysis among all of the obtained indices. Nevertheless, when pooling the indices for all the subjects, the inter-subject variability causes a moderate classification performance for all indices. In this case, the best performing index is fnQ with an area under the receiver operator curve (AUC) of 75%, 77%, 79%, and 80% for the 30s, 60s, 120s, and 240s time windows respectively.