751 Heart Rate and Heart Rate Variability During Sleep As Biomarkers for Depression

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A293-A293
Author(s):  
Massimiliano Grassi ◽  
Daniela Caldirola ◽  
Silvia Daccò ◽  
Giampaolo Perna ◽  
Archie Defillo

Abstract Introduction Evidence suggests a high prevalence of depression in subjects with Sleep-Wake Disorders, with impaired sleep being both a risk factor and a symptom of depression. However, depression currently remains for the most undiagnosed in this population, which can lead to a lack or delay in the treatment, and ultimately contribute to chronicity, recurrence of depression, and increase risk of suicide. Depression is characterized by alteration in sleep architecture and imbalanced autonomic nervous system function, and specific alteration may serve as biomarkers to identify ongoing depression in subjects with Sleep-Wake Disorders undergoing polysomnography. Thus, the aim of this study is to investigate differences in sleep architecture and autonomic modulation, measured by heart rate and heart rate variability throughout sleep stages, in subjects undergoing polysomnography in a sleep clinic. Methods A preliminary sample of forty subjects undergoing polysomnography was recruited in three different sleep clinics. The Patient Health Questionnaire–9 was administered to participants before the beginning of the sleep study. A cut-off of 10 was applied to identify subjects with possible current depression. The polysomnography recordings were processed with the MEBsleep software (Medibio Limited) which automatically calculats sleep architecture indices, and heart rate and heart rate variability parameters throughout sleep stages. The Mann-Whitney U test was used to investigate differences between the depressed and non-depressed groups. Results Possible current depression was found in fourteen subjects (35%). These Subjects had statistically significant higher heart rate (median depressed=78.01, median non-depressed=64.61, p=0.01) and lower Root Mean Square of the Successive Difference (RMSSD; median depressed=18.41 ms, median non-depressed=26.52 ms, p=0.02), number of pairs of successive NN intervals that differ by more than 50 ms (pNN50. Median depressed=1.62%; median non-depressed=5.64%; p=0.03), and High Frequency (absolute power) in REM (median depressed=104.17 ms2; median non-depressed=214.58 ms2; p=0.03) than those without depression. No significant differences resulted in the sleep architecture indices. Conclusion These results preliminary indicates a decreased parasympathetic activity in subjects with possible depression during REM, suggesting that heart rate and heart rate variability during sleep may be used as biomarkers to identify current depression in subjects undergoing polysomnography in sleep clinics. Support (if any):

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Qian-Qian Li ◽  
Guang-Xia Shi ◽  
Xin-Xing Fu ◽  
Li-Li Han ◽  
Li-Ying Liu ◽  
...  

Background. Recent reports suggest that a proportion of tinnitus patients suffer from mental illness. Autonomic nervous system plays a useful role in tinnitus therapy since electrical vagal nerve stimulation (VNS) has been frequently used to alleviate tinnitus-induced depression in clinic. heart rate variability (HRV), which is reflective of autonomic nervous system function, has been proved to be modulated by acupuncture. In the present study, we aim to compare the effect of deqi sensation on heart rate variability in adult tinnitus patients.Methods. Thirty participants are randomly assigned to verum acupuncture (creating deqi) or shallow acupuncture (not creating deqi) at Baihui (Du-20), Shenting (Du-24), Tinghui (GB-2), Waiguan (SJ-5), and Zulinqi (GB-41) for 3 weeks. The primary outcome measure is heart rate variability, which is measured at the first acupuncture, as well as the last acupuncture.Discussion. Completion of this trial will help to identify the role of deqi sensation in acupuncture effect for tinnitus and reveal an autonomic modulation mechanism for acupuncture effect.Trial Registration. This trial is registered with International Standard Randomised Controlled Trial NumberISRCTN58013563.


2016 ◽  
Vol 26 (2) ◽  
pp. 023101 ◽  
Author(s):  
Mateusz Soliński ◽  
Jan Gierałtowski ◽  
Jan Żebrowski

2016 ◽  
Vol 20 (3) ◽  
pp. 975-985 ◽  
Author(s):  
Ren-Jing Huang ◽  
Ching-Hsiang Lai ◽  
Shin-Da Lee ◽  
Wei-Che Wang ◽  
Ling-Hui Tseng ◽  
...  

SLEEP ◽  
2013 ◽  
Vol 36 (12) ◽  
pp. 1919-1928 ◽  
Author(s):  
Philippe Boudreau ◽  
Wei-Hsien Yeh ◽  
Guy A. Dumont ◽  
Diane B. Boivin

1999 ◽  
Vol 45 (10) ◽  
pp. 1313-1320 ◽  
Author(s):  
Eileen P Sloan ◽  
Madhu Natarajan ◽  
Brian Baker ◽  
Paul Dorian ◽  
Dmitry Mironov ◽  
...  

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.


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