Key result
ARIMA analysis of SDNN detects early warning signals of impending sleep onset in ~92% of cases.
Why the study?
Prior studies reported divergent findings regarding heart rate variability during drowsiness.
Does an algorithm based on ARIMA analysis of SDNN detect early warning signals of impending sleep onset in healthy individuals?
Observational (n=25)
Does an algorithm based on ARIMA analysis of SDNN detect early warning signals of impending sleep onset in healthy individuals?
An algorithm based on ARIMA analysis of heart rate variability (SDNN) can successfully detect early warning signals of impending sleep onset in 92% of cases.
Answers are grounded in this paper and cite their sources.
May support CSD-ARIMA detection of drowsiness via SDNN; leaves open validation in larger clinical cohorts before any practice consideration.
Given the divergent findings regarding heart rate variability during drowsiness in studies, we propose utilizing the concept of critical slowing down (CSD) to detect early warning signals (EWS) of impending sleep onset based on heart rate variability as measured by standard deviation of the interbeat interval between normal peaks (SDNN). To expedite the detection of such EWS, we suggest employing ARIMA models. The study involved 25 healthy individuals (10 males) aged 20–35 years. Heart rate was recorded in natural home-based conditions from 19:40 to 06:00. The moment of sleep onset was recorded using the continuous button pressure paradigm. The results indicated that SDNN nonlinearly changed approaching sleep onset and exhibited spikes interpretable as EWS. SDNN was successfully forecasted using ARIMA analysis within a 10 minute window. An algorithm was developed to determine EWS signaling the impending transition to sleep based on ARIMA analysis of SDNN within the CSD concept. The algorithm allows for the detection of such EWS in 92% of cases.
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Демарева et al. (2023) conducted an observational in Healthy individuals (n=25). ARIMA analysis of SDNN was evaluated on Detection of early warning signals (EWS) of impending sleep onset. An algorithm based on ARIMA analysis of SDNN within the critical slowing down concept successfully detected early warning signals of impending sleep onset in 92% of cases.
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