Analysis of heart rate indices at different levels of sleepiness
- Authors: Demareva V.A.1
-
Affiliations:
- National Research Lobachevsky State University of Nizhny Novgorod
- Issue: Vol 18, No 4 (2023)
- Pages: 610-613
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623328
- DOI: https://doi.org/10.17816/gc623328
- ID: 623328
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Abstract
The objective detection of sleepiness through physiological indices is crucial for ensuring transportation and industrial safety. As gadgets become more commonplace for measuring heart rate, it is important to identify valid indices that correlate with the level of sleepiness. Research indicates that time-domain and frequency-domain heart rate indices align with levels of alertness and sleepiness [e.g., 1, 2]. The aim of this paper is to identify heart rate indices that correlate with varying degrees of evening sleepiness in vivo, whether low or high.
The data analyzed in this paper was obtained from 32 recordings from the SSDD (Subjective Sleepiness Dynamics Dataset) collected at the UNN Cyberpsychology Laboratory since 2022. The experiment was designed in the following manner: participants put on a heart rhythm sensor (Polar H10) at 7:40 PM in their homes and connected it to a smartphone app. Participants then completed a self-reported questionnaire in the electronic system. Starting at 8:00 PM, and every 30 minutes thereafter, the participant recorded data on the subjective level of sleepiness via the Karolinska (KSS) and Stanford (SSS) sleepiness scales up until the time of the participant’s bedtime. The selection of 32 recordings met specific criteria: all participants went to bed between 10:30 and 11:00 PM and completed both the KSS and SSS on time at each time point (08:00, 08:30, 09:00, 09:30, and 10:00 PM).
For each time point, an integral sleepiness score was calculated according to the formula:
sl=(KSS/10+SSS/7)/2.
The 4-minute rhythmogram recordings corresponding to the time points of KSS and SSS filling were chosen for analysis. Data were processed in Jupyter Notebook. Heart rhythm indices, including time-domain, frequency-domain, and nonlinear indices, were computed for each time point from the rhythmogram — NN-interval sequence — using the “hrv-analysis” package. The study employed Student’s t-test to compare indices for varying levels of sleepiness based on integral scores, where “low level” was defined as sl <0.45 and “high level” was defined as sl >0.55. Additionally, the Pearson criterion was used to evaluate the correlation between sl and heart rate indices at each time interval.
The indices responsible for the variability of the NN-interval sequence, namely NNi_50 (p=0.027), NNi_20 (p=0.007), and pNNi_20 (p=0.024), exhibited lower values during periods of “low” sleepiness (N=54) as compared to “high” sleepiness (N=58). Moreover, we observed that the autonomic balance index, i.e., the ratio of the power of the heart rate variability spectrum in the low-frequency band to that in the high-frequency band, was higher during ‘high’ sleepiness (p=0.015). Analysis revealed that correlations between integral sleepiness score and heart rate indices were present only for the 08:30 PM time point. Correlations of sl with the NN-interval range (R=–0.388; p=0.028), the severity of sympathetic regulatory circuit activity (R=–0.383; p=0.031), and the nonlinear cardiovagal index (R=–0.359; p=0.043) were found.
Thus, it can be inferred that increased sleepiness is associated with decreased heart rate variability indices, along with decreased functioning of the autonomic nervous system overall.
Full Text
The objective detection of sleepiness through physiological indices is crucial for ensuring transportation and industrial safety. As gadgets become more commonplace for measuring heart rate, it is important to identify valid indices that correlate with the level of sleepiness. Research indicates that time-domain and frequency-domain heart rate indices align with levels of alertness and sleepiness [e.g., 1, 2]. The aim of this paper is to identify heart rate indices that correlate with varying degrees of evening sleepiness in vivo, whether low or high.
The data analyzed in this paper was obtained from 32 recordings from the SSDD (Subjective Sleepiness Dynamics Dataset) collected at the UNN Cyberpsychology Laboratory since 2022. The experiment was designed in the following manner: participants put on a heart rhythm sensor (Polar H10) at 7:40 PM in their homes and connected it to a smartphone app. Participants then completed a self-reported questionnaire in the electronic system. Starting at 8:00 PM, and every 30 minutes thereafter, the participant recorded data on the subjective level of sleepiness via the Karolinska (KSS) and Stanford (SSS) sleepiness scales up until the time of the participant’s bedtime. The selection of 32 recordings met specific criteria: all participants went to bed between 10:30 and 11:00 PM and completed both the KSS and SSS on time at each time point (08:00, 08:30, 09:00, 09:30, and 10:00 PM).
For each time point, an integral sleepiness score was calculated according to the formula:
sl=(KSS/10+SSS/7)/2.
The 4-minute rhythmogram recordings corresponding to the time points of KSS and SSS filling were chosen for analysis. Data were processed in Jupyter Notebook. Heart rhythm indices, including time-domain, frequency-domain, and nonlinear indices, were computed for each time point from the rhythmogram — NN-interval sequence — using the “hrv-analysis” package. The study employed Student’s t-test to compare indices for varying levels of sleepiness based on integral scores, where “low level” was defined as sl <0.45 and “high level” was defined as sl >0.55. Additionally, the Pearson criterion was used to evaluate the correlation between sl and heart rate indices at each time interval.
The indices responsible for the variability of the NN-interval sequence, namely NNi_50 (p=0.027), NNi_20 (p=0.007), and pNNi_20 (p=0.024), exhibited lower values during periods of “low” sleepiness (N=54) as compared to “high” sleepiness (N=58). Moreover, we observed that the autonomic balance index, i.e., the ratio of the power of the heart rate variability spectrum in the low-frequency band to that in the high-frequency band, was higher during ‘high’ sleepiness (p=0.015). Analysis revealed that correlations between integral sleepiness score and heart rate indices were present only for the 08:30 PM time point. Correlations of sl with the NN-interval range (R=–0.388; p=0.028), the severity of sympathetic regulatory circuit activity (R=–0.383; p=0.031), and the nonlinear cardiovagal index (R=–0.359; p=0.043) were found.
Thus, it can be inferred that increased sleepiness is associated with decreased heart rate variability indices, along with decreased functioning of the autonomic nervous system overall.
ADDITIONAL INFORMATION
Funding sources. This work was supported by the Russian Science Foundation, grant No. 22-28-20509, https://rscf.ru/project/22-28-20509/
About the authors
V. A. Demareva
National Research Lobachevsky State University of Nizhny Novgorod
Author for correspondence.
Email: valeriia.demareva@fsn.unn.ru
Russian Federation, Nizhny Novgorod
References
- Awais M, Badruddin N, Drieberg M. A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors. 2017;17(9):1991. doi: 10.3390/s17091991
- Chua ECP, Tan WQ, Yeo SC, et al. Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation. Sleep. 2012;35(3):325–334. doi: 10.5665/sleep.1688
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