Heart Rate Variability Assessment by the Entropy Parameters during Sleep
Dong-Kyu Kim1, Jae Mok Ahn2
1Prof. Jae Mok Ahn* Ph.D. School of Software, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea.
2Prof. Dong-Kyu Kim, M.D. Ph.D. Dept. of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea.
Manuscript received on June 14, 2020. | Revised Manuscript received on June 28, 2020. | Manuscript published on July 10, 2020. | PP: 468-472 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7230079920 | DOI: 10.35940/ijitee.I7230.079920
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The nonlinear heart rate variability (HRV) parameter quantifies autonomic nervous system (ANS) activity based on the complexity or irregularity of an HRV dataset. At present, among various entropy-related parameters during sleep, approximate entropy (ApEn) and sample entropy (SampEn) are not as well understood as other entropy parameters such as Shannon entropy (SE) and conditional entropy (CE). Therefore, in this study, we investigated the characteristics of ApEn and SampEn to differentiate a rapid eye movement (REM) and nonrapid eye movement (NREM) for sleep stages. For nonlinear sleep HRV analysis, two target 10-minute, long-term HRV segments were obtained from each REM and NREM for 16 individual subjects. The target HRV segment was analyzed by moving the 2-minute window forward by 2 s, resulting in 240 results of each ApEn and SampEn. The ApEn and SampEn were averaged to obtain the mean value and standard deviation (SD) of all the results. SampEn provides excellent discrimination performance between REM and NREM in terms of the mean and SD (p<0.0001 and p=0.1989, respectively; 95% CI), but ApEn was inferior to SampEn (p=0.1980 and p=0.9931). The results indicate that SampEn, but not ApEn could be used to discriminate REM from NREM and detect various sleep-related incidents.
Keywords: Heart rate variability, Approximate entropy, Sample entropy, Autonomic nervous system, Sleep.
Scope of the Article: Autonomic Computing and Agent-Based Systems