Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring

Abstract Introduction Heart rate variability (HRV) is commonly used to assess the activity of the autonomic nervous system (ANS). ANS function changes, reflected in HRV, result from factors including lifestyle, aging, cardiorespiratory illnesses, sleep state, and physiological stress. Despite broad interest in HRV, few studies have established normative overnight HRV values for a large population. To better understand population level HRV changes, ecologically-valid, overnight sleep SDNN (standard deviation of all normal heartbeat intervals, lower HRV is reflected by lower SDNN) values have been analyzed for a large sample of Sleep Number 360 smart bed users. Methods Overnight SDNN values were obtained over the course of 18.2M sleep sessions from 379,225 sleepers (48 ± 14.7 sessions/user). 50.9 percent of sleepers were female. The age was normally distributed with mean ± SD of 52.8 ± 12.7 years (range 21 to 84). Heartbeat intervals used to compute SDNN were extracted from a ballistocardiogram (BCG). BCG-based HRV estimation during sleep has previously been validated against ECG-based HRV with an R-square of 0.5. Results Using a Generalized Linear Model, significant cross-sectional associations with SDNN were observed for three variables of interest: age, gender, and day-of-the-week. For sleepers under 50, SDNN declined at a rate of about 2.1 ms/year, then leveled off for sleepers aged 50-65, and increased slightly thereafter. Women under 50 displayed lower, more slowly declining, SDNN values than men, but this trend reversed for sleepers over 50. Throughout the week, SDNN values followed a U-shaped (women) or L-shaped (men) pattern, where values were highest during the weekend and lowest at mid-week. Conclusion Using a smart bed to unobtrusively measure overnight SDNN values for a large set of sleepers in an ecologically valid environment, reveals significant effects of age, gender, and day of the week on overnight SDNN. Support (if any):

Download Full-text

Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

Frontiers in Neurology ◽

10.3389/fneur.2021.691328 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xi Fang ◽

Hong-Yun Liu ◽

Zhi-Yan Wang ◽

Zhao Yang ◽

Tung-Yang Cheng ◽

...

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Nerve Stimulation ◽

Vagus Nerve Stimulation ◽

Outcome Prediction ◽

Healthy Controls ◽

Drug Resistant ◽

Sleep State ◽

Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

Download Full-text

Sleep state instabilities in patients with periodic limb movements in sleep - Detection and quantification with heart rate variability

Psychiatry Research ◽

10.1016/j.psychres.2020.113454 ◽

2020 ◽

Vol 293 ◽

pp. 113454

Author(s):

Chen-Yu Lin ◽

Shih-Jen Tsai ◽

Chung-Kang Peng ◽

Albert C. Yang

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Periodic Limb Movements ◽

Limb Movements ◽

Sleep State ◽

Detection And Quantification

Download Full-text

Analysis on power spectrum and base-scale entropy for heart rate variability signals modulated by reversed sleep state

Acta Physica Sinica ◽

10.7498/aps.63.198703 ◽

2014 ◽

Vol 63 (19) ◽

pp. 198703

Author(s):

Liu Da-Zhao ◽

Wang Jun ◽

Li Jin ◽

Li Yu ◽

Xu Wen-Min ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Power Spectrum ◽

Sleep State

Download Full-text

Sleep in the Intensive Care Unit through the Lens of Breathing and Heart Rate Variability: A Cross-Sectional Study

10.1101/2021.09.23.21264039 ◽

2021 ◽

Author(s):

Wolfgang Ganglberger ◽

Parimala Velpula Krishnamurthy ◽

Syed A. Quadri ◽

Ryan A. Tesh ◽

Abigail A. Bucklin ◽

...

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Heart Rate Variability ◽

Intensive Care ◽

Adult Patients ◽

Sleep Stages ◽

Sleep Laboratory ◽

Sleep State ◽

Sleep Physiology ◽

Medical Icus

Background. Full polysomnography, the gold standard of sleep measurement, is impractical for widespread use in the intensive care unit (ICU). Wrist-worn actigraphy and subjective sleep assessments do not measure sleep physiology adequately. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods. Methods. We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in covariate-matched sleep laboratory patients. We analyzed the agreement of the determined sleep stages between the HRV- and breathing-based models, computed sleep indices, and quantified breathing variables during sleep. Results. We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p<0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median = 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median = 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients. Conclusions. Cardiovascular and respiratory signals encode sleep state information, which can be utilized to measure sleep state in the ICU. Using these easily measurable variables can provide automated information about sleep in the ICU.

Download Full-text

Impact of stroke on diurnal heart rate variability in rats

10.1101/071555 ◽

2016 ◽

Author(s):

Sean R. Huff ◽

Samreen Ahmed ◽

He Meng ◽

Tiecheng Liu ◽

Jimo Borjigin ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Ischemic Stroke ◽

Experimental Stroke ◽

Cardiac Events ◽

Sleep State ◽

Frequency Domain Methods ◽

Continuous Eeg ◽

Hrv Analysis ◽

The Right

ABSTRACTIn humans, ischemic stroke is associated with reduced heart rate variability (HRV), a predictor of neurogenic cardiac events. This study was conducted to determine whether experimental stroke preferentially affects HRV during specific times of the day or parts of the sleep-wake cycle. A continuous telemetry system was used to analyze electroencephalograms (EEG), electromyograms (EMG), and electrocardiograms (ECG) after experimental ischemic stroke in rats (n = 13). Animals were implanted with telemetry probes and, after a two week stabilization period, 72h of baseline continuous EEG, EMG, ECG data were collected. Animals were then assigned to two procedure groups: 1) experimental stroke via 90 minute intraluminal filament occlusion of the right middle cerebral artery, or 2) sham surgery. Continuous waveform recording and HRV analysis was then continued for 48h following each procedure. HRV analysis was conducted by both time-domain and frequency-domain methods. Heart rate exhibited circadian rhythms among all animals prior to and following both procedures. Neither procedure contributed to significant heart rate changes. We did not find sleep state or circadian-dependent alterations in HRV in baseline recording. However, after stroke, animals exhibited elevated high frequency (HF) HRV (>10% increase, p<0.05) specifically during daytime waking periods. This contrasts with studies in humans that demonstrate reduced HRV after stroke, suggesting that age, medical comorbidities, or species differences modify the overall response to cortical infarction of autonomic control centers.

Download Full-text

666 Sleep Architecture in the Intensive Care Unit As Revealed via Breathing and Heart Rate Variability

SLEEP ◽

10.1093/sleep/zsab072.664 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A260-A261

Author(s):

Wolfgang Ganglberger ◽

Parimala Velpula Krishnamurthy ◽

Abigail Bucklin ◽

Ryan Tesh ◽

Madalena Da Silva Cardoso ◽

...

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Heart Rate Variability ◽

Intensive Care ◽

Massachusetts General Hospital ◽

Pharmaceutical Drugs ◽

Respiratory Effort ◽

Neural Network Models ◽

Sleep State ◽

Age And Sex

Abstract Introduction Sleep in the intensive care unit (ICU) is difficult to measure by conventional polysomnography. We investigated the feasibility of assessing sleep state from readily available ICU signals: heart rate variability (HRV) from electrocardiography and breathing from a wearable respiratory band. We compared findings with an age and sex matched sleep laboratory group. Methods As part of a clinical trial, 102 adult non-ventilated patients in three ICUs in the Massachusetts General Hospital wore a respiratory band. Both heart rate variability (RR-intervals) from ECG, and breathing (respiratory effort waveforms) data for up to seven days per patient were obtained. 220 age- and sex-matched subjects from a sleep lab cohort who wore the same respiratory effort band and ECG were selected for comparison. We staged sleep from the HRV and breathing data using previously published deep neural network models. We defined discordant sleep epochs as those where HRV- and breathing-based models disagreed. Agreement was computed for the following pairs: (R,R),(N1,N1),(N2,N2),(N3,N3),(N1,W),(N1,N2),(N2,N3). Results Demographics: Mean(STD) age: ICU 68(9), sleeplab 68(9); BMI: ICU 27(6), sleeplab 31(6); ICU 40% female, sleeplab 44% female; race: ICU%:Sleeplab% 90:69 White, 5:4 Black, 2:7 Asian. 34% of ICU-subjects were in a medical ICU, 66% in a surgical ICU. Mean total sleep duration in the ICU was 8.9 hours (4.5h concordant, 4.4h discordant sleep). We observed increased amounts of discordant sleep in the ICU compared with the sleeplab cohort (4.4h vs. 1h, p<0.01). We found different REM sleep distributions (p<0.01) with reduced median (10% vs. 20%) but elevated 90% quantile (45% vs. 26%), elevated N1(%) (41% vs. 26%, p<0.05), reduced N2(%) (19 vs. 44, p<0.01), and reduced N2+N3(%) (34 vs. 59, p<0.05). We further observed higher mean respiratory rate (17.4 vs. 15.9 breaths per minute, p<0.01), lower inter-breath-intervals (3.9 vs. 4.7 seconds per breath, p<0.01), and more breathing variability than in sleeplab AHI<5 group but less than in AHI>15 group. Conclusion HRV and respiratory-based measures can assess sleep in the ICU. The findings of increased discordant sleep in the ICU might stem from limitations of the models, fundamental changes in sleep biology during critical illness, pharmaceutical drugs, sleep fragmentation, and/or associated pathology in the ICU. Support (if any):

Download Full-text