A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification
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ABSTRACT:
SUBMITTER: Chung Y
PROVIDER: S-EPMC7959762 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
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Project description:Heart rate variability (hrv) is a physiological phenomenon of the variation in the length of the time interval between consecutive heartbeats. In many cases it could be an indicator of the development of pathological states. The classical approach to the analysis of hrv includes time domain methods and frequency domain methods. However, attempts are still being made to define new and more effective hrv assessment tools. Persistent homology is a novel data analysis tool developed in the recent decades that is rooted at algebraic topology. The Topological Data Analysis (TDA) approach focuses on examining the shape of the data in terms of connectedness and holes, and has recently proved to be very effective in various fields of research. In this paper we propose the use of persistent homology to the hrv analysis. We recall selected topological descriptors used in the literature and we introduce some new topological descriptors that reflect the specificity of hrv, and we discuss their relation to the standard hrv measures. In particular, we show that this novel approach provides a collection of indices that might be at least as useful as the classical parameters in differentiating between series of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering from a stroke episode.
| S-EPMC8297888 | biostudies-literature
Project description:STUDY OBJECTIVES:Sleep-wake regularity (SWR) is often disrupted in college students and mood disorders are rife at this age. Disrupted SWR can cause repetitive and long-term misalignment between environmental and behavioral cycles and the circadian system which may then have psychological and physical health consequences. We tested whether SWR was independently associated with mood and autonomic function in a healthy adult cohort. METHODS:We studied 42 college students over a 3 week period using daily sleep-wake diaries and continuous electrocardiogram recordings. Weekly SWR was quantified by the interdaily stability of sleep-wake times (ISSW) and mood was assessed weekly using the Beck Depression Inventory-II. To assess autonomic function, we quantified the high-frequency (HF) power of heart rate variability (HRV). Linear mixed effects models were used to assess the relationship between repeated weekly measures of mood, SWR, and HF. RESULTS:Low weekly ISSW predicted subsequent poor mood and worsening mood independently of age, sex, race, sleep duration, and physical activity. Although no association was found between ISSW and HF, the ISSW-mood association was significantly moderated by nocturnal HF, i.e. reported mood was lowest after a week with low ISSW and high HF. Prior week mood scores did not significantly predict the subsequent week's ISSW. CONCLUSIONS:Irregular sleep-wake timing appears to precede poor mood in young adults. Further work is needed to understand the implications of high nocturnal HRV in those with low mood and irregular sleep-wake cycles.
| S-EPMC6519914 | biostudies-literature
Project description:IntroductionPerinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV).MethodsNine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested.Results and discussionIn the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.
| S-EPMC10322181 | biostudies-literature
Project description:In France, new cancer cases keep on increasing with around 150 000 deaths yearly. Cancer therapy research is constantly evolving. Indeed, several studies explore new treatments or their combination with conventional cancer treatments. But, at the same time, complementary and alternative medicines, as osteopathy, remain little explored upon their role in the combination with conventional therapy.
Several studies showed indirect interaction between vagus nerve and cancer. Firstly, vagus nerve regulates homeostasis and immunity by reducing systemic inflammation while maintaining local inflammation and antitumor effects. Secondly, vagus nerve stimulation increases Heart Rate Variability (HRV). Moreover, a higher HRV is associated with an improvement of vital prognosis in cancer patients. Vagus nerve could be stimulated by noninvasive osteopathic manipulations.
This prospective, monocentric and randomized study is a collaboration between the Centre Hospitalier d’Avignon and the Institut de Formation en Ostéopathie du Grand Avignon. It focuses on using noninvasive osteopathic mobilizations to stimulate vagus nerve. Indeed, this study aims to evaluate effects of vagus nerve osteopathic stimulations on HRV in patients with lung cancer, colorectal cancer, Non Hodgkin Lymphoma or Multiple Myeloma. More specifically, this study will tell us whether vagus nerve noninvasive osteopathic stimulations induce increase of HRV associated with a decrease of systemic inflammation and an improvement of patient’s quality of life.
| 2364606 | ecrin-mdr-crc
Project description:OBJECTIVE:To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. DESIGN:Multicenter, pilot study. SETTING:Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS:We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives. MEASUREMENTS AND MAIN RESULTS:As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%. CONCLUSIONS:With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under sedation.
| S-EPMC5474145 | biostudies-literature
Project description:The domestic dog (Canis familiaris) has been shown to both excel in recognising human emotions and produce emotion-related vocalisations and postures that humans can easily recognise. However, little is known about the effect of emotional experiences on subsequent sleep physiology, a set of phenomena heavily interrelated with emotions in the case of humans. The present paper examines heart rate (HR) and heart rate variability (HRV) during dogs' sleep, measures that are influenced by both positive and negative emotions in awake dogs. In Study I, descriptive HR and HRV data is provided on N = 12 dogs about the different sleep stages (wake, drowsiness, non-rapid eye movement (non-REM), REM; scoring based on electroencephalogram (EEG) data). We conclude that wakefulness is characterised by higher HR and lower HRV compared to all sleep stages. Furthermore, drowsiness is characterised by higher HR and lower HRV than non-REM and REM, but only if the electrocardiogram (ECG) samples are taken from the first occurrence of a given sleep stage, not when the longest periods of each sleep stage are analysed. Non-REM and REM sleep were not found to be different from each other in either HR or HRV parameters. In Study II, sleep HR and HRV measures are compared in N = 16 dogs after a positive versus negative social interaction (within-subject design). The positive social interaction consisted of petting and ball play, while the negative social interaction was a mixture of separation, threatening approach and still face test. Results are consistent with the two-dimensional emotion hypothesis in that following the intense positive interaction more elevated HR and decreased HRV is found compared to the mildly negative (lower intensity) interaction. However, although this trend can be observed in all sleep stages except for REM, the results only reach significance in the wake stage. In sum, the present findings suggest that HR and HRV are possible to measure during dogs' sleep, and can potentially be used to study the effect of emotions not only during but also after such interactions.
| S-EPMC6071078 | biostudies-literature
Project description:We developed a cross-over study design with two interventions in randomized order to compare the effects of sleep fragmentation and partial sleep restriction on cardiac autonomic tone. Twenty male subjects (40.6 ± 7.5 years old) underwent overnight polysomnography during 2 weeks, each week containing one undisturbed baseline night, one intervention night (either sleep restriction with 5 h of sleep or sleep fragmentation with awakening every hour) and two undisturbed recovery nights. Parameters of heart rate variability (HRV) were used to assess cardiac autonomic modulation during the nights. Sleep restriction showed significant higher heart rate (p = 0.018) and lower HRV-pNN50 (p = 0.012) during sleep stage N1 and lower HRV-SDNN (p = 0.009) during wakefulness compared to the respective baseline. For HR and SDNN there were recovery effects. There was no significant difference comparing fragmentation night and its baseline. Comparing both intervention nights, sleep restriction had lower HRV high frequency (HF) components in stage N1 (p = 0.018) and stage N2 (p = 0.012), lower HRV low frequency (LF) (p = 0.007) regarding the entire night and lower SDNN (p = 0.033) during WASO during sleep. Sleep restriction increases sympathetic tone and decreases vagal tone during night causing increased autonomic stress, while fragmented sleep does not affect cardiac autonomic parameters in our sample.
| S-EPMC10110519 | biostudies-literature
Project description:Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep). Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm. Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS. Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.
| S-EPMC5767731 | biostudies-literature
Project description:PurposeAutonomic dysfunction has been reported in autism spectrum disorders (ASD). Less is known about autonomic function during sleep in ASD. The objective of this study is to provide insight into the autonomic cardiovascular control during different sleep stages in ASD. We hypothesized that patients with ASD have lower vagal and higher sympathetic modulation with elevated heart rate, as compared to typical developing children (TD).MethodsWe studied 21 children with ASD and 23 TD children during overnight polysomnography. Heart rate and spectral parameters were calculated for each vigilance stage during sleep. Data from the first four sleep cycles were used to avoid possible effects of different individual sleep lengths and sleep cycle structures. Linear regression models were applied to study the effects of age and diagnosis (ASD and TD).ResultsIn both groups, HR decreased during non-REM sleep and increased during REM sleep. However, HR was significantly higher in stages N2, N3 and REM sleep in the ASD group. Children with ASD showed less high frequency (HF) modulation during N3 and REM sleep. LF/HF ratio was higher during REM. Heart rate decreases with age at the same level in ASD and in TD. We found an age effect in LF in REM different in ASD and TD.ConclusionOur findings suggest possible deficits in vagal influence to the heart during sleep, especially during REM sleep. Children with ASD may have higher sympathetic dominance during sleep but rather due to decreased vagal influence.
| S-EPMC5106315 | biostudies-literature
Project description:To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients.Multicenter, pilot study.Several ICUs at Massachusetts General Hospital, Boston, MA.Electrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system.Richmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted "unarousable" (Richmond Agitation- Sedation Scale = -5, -4), "sedated" (-3, -2, -1), "awake" (0), "agitated" (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale < 0) and nonsedated states (Richmond Agitation-Sedation Scale > 0).With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.
| S-EPMC4987179 | biostudies-literature