A. Baharav

Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability.

Objective The use of an efficient noninvasive method to investigate the autonomic nervous system and cardiovascular control during sleep. Background Beat-to-beat heart rate variability displays two main components: a low-frequency (LF) one representing sympathetic and parasympathetic influence and a high-frequency (HF) component of parasympathetic origin. Sympathovagal balance can be defined as LF/HF ratio. Methods/design We reviewed normal, standardly staged all-night polysomnograms from 10 healthy children aged 6 to 17 years. Recorded 256-second traces of heart rate and respiration were sampled. Power spectra of instantaneous heart rate and respiration were computed using a fast Fourier transform method. Results The study revealed a decrease in LF during sleep, with minimal values during non-REM slow-wave sleep and elevated levels similar to those of wakefulness during REM. HF increased with sleep onset, reaching maximal values during slow-wave sleep, and behaved as a mirror image of LF. LF/HF ratio displayed changes similar to those in LF. Conclusion The sympathetic predominance that characterizes wakefulness decreases during non-REM sleep, is minimal in slow-wave sleep, and surges toward mean awake levels during REM sleep. The autonomic balance is shifted toward parasympathetic predominance during slow-wave sleep. This noninvasive method used to outline autonomic activity achieves results that are in complete agreement with those obtained with direct invasive tools.

Autonomic cardiovascular control in children with obstructive sleep apnea

Autonomic cardiorespiratory control changes with sleep-wake states and is influenced by sleep-related breathing disorders. Power spectrum (PS) analysis of instantaneous fluctuations in heart rate (HR) is used to investigate the role of the autonomic nervous system (ANS) in cardiorespiratory control. The two spectral regions of interest are the low frequency component (LF) and high frequency component (HF).The aim of the present study was to investigate the autonomic cardiorespiratory control in children with obstructive sleep apnea (OSA) syndrome. We studied 10 children with OSA versus 10 normal children. All subjects underwent whole night polysomnography. Spectral analysis of the HR and breathing signals was performed for 256 second long, artifact-free epochs in each sleep-wake state. The LF power was higher in the OSA group compared with control subjects for all states, reflecting enhanced sympathetic activity in OSA subjects. The results indicated sympathetic predominance during REM sleep in all subjects and parasympathetic predominance in slow wave sleep only in controls. The autonomic balance (LF/HF) was significantly higher in OSA patients than in control subjects, at all stages during night sleep, and while awake before sleep onset. An index of overall autonomic balance (ABI) was computed for each subject and correlated well with the measured respiratory disturbance index (RDI).

Autonomic changes during wake–sleep transition: A heart rate variability based approach

Autonomic function during sleep and wakefulness has been extensively investigated, however information concerning autonomic changes during the wake to sleep transition is scarce. The objective of the present study was to non-invasively characterize autonomic function and additional physiologic changes during sleep onset in normal and abnormal sleep. The estimation of autonomic function was based on time-frequency analysis of the RR interval series, using the power components in the very-low-frequency range (0.005-0.04 Hz), low-frequency (0.04-0.15 Hz), and high-frequency range (0.15-0.5 Hz). The ratio of low to high frequency power represented the sympathovagal balance. Thirty-four subjects who underwent whole night polysomnography were divided into 3 groups according to their complaints and study results: normal subjects, apneic patients (OSAS), and subjects with various sleep disorders (VSD). The results indicated a significant increase in RR interval during sleep onset, although its variability decreased; respiratory rate did not change, yet respiration became more stable; EMG amplitude and its variability decreased with sleep onset. Very-low-frequency power started to decrease significantly 2 min before sleep onset in all groups; low-frequency power decreased and high-frequency power did not change significantly in all groups, accordingly their ratio decreased and reflected a shift towards parasympathetic predominance. Although autonomic function displayed similar behavior in all subjects, OSAS and VSD patients presented a higher sympathovagal balance reflecting enhanced sympathetic predominance in those groups compared to normal subjects, both before and after sleep onset. All parameters reached a nadir at a defined time point during the process of falling asleep. We conclude that the wake-sleep transition period represents a transitional process between two physiologically different states; this transition starts with a decrease in the very slow oscillations in heart rate that anticipates a step-change resetting of autonomic function, followed by a decrease in sympathovagal balance towards the end of the process.

Obstructive sleep apnea detection based on electrocardiogram analysis

Obstructive Sleep Apnea is a frequent disorder with detrimental health, performance and safety effects. The diagnosis of the disorder is cumbersome and expensive. New methods for screening and diagnosis are needed. The method the authors describe in this work is based on detection of respiratory disturbance during sleep from continuously monitored ECG during the night. The detection is based on QRS complex changes caused by apneas and spectral abnormalities in the Heart Rate Variability which are related to recurrent respiratory events. An automatic step-by-step approach, based on these two already described phenomena, was developed, validated and then applied on data supplied by Physionet for the CinC Challenge 2000. The methods yield results in excellent agreement with the manually scored standard sleep studies, and achieved 14788/17262 correct classifications on minute-by-minute bases.

Early detection of falling asleep at the wheel: A Heart Rate Variability approach

In this study we check the feasibility of a new ECG-based approach to detect driverspsila propensity to fall asleep at the wheel. Ten healthy volunteers, under conditions of increasing sleep deprivation (up to 34 hours), were asked to alternately undergo a Maintenance of Wakefulness Test or a Driving Simulation test every 2 hours while ECG, EEG, EMG, eye movement and video were recorded. Results from 59 falling asleep (FA) events tracked from the first 5 volunteers during MWT provide promising trends: Heart Rate Variability in the VLF range decreases consistently and significantly minutes before FA events. The sympatho-vagal balance is very low compared to baseline wake values for about 5 minutes before the events. The mean HR and overall RR variability decrease during FA events by 2.2 SD and 2.9 SD below regional means. These changes found during MWT suggest that ECG derived parameters in the time and time-frequency domains may provide a useful tool for monitoring driverspsila drowsiness and preventing traffic accidents.

Electrocardiogram derived respiration during sleep

The aim of this study was to quantify the ECG Derived Respiration (EDR) in order to extend the capabilities of ECG-based sleep analysis. We examined our results in normal subjects and in patients with Obstructive Sleep Apnea Syndrome (OSAS) or Central Sleep Apnea. Lead 2 ECG and three measures of respiration (thorax and abdominal effort, and oronasal flow signal) were recorded during sleep studies of 12 normal and 12 OSAS patients. Three parameters, the R-wave amplitude (RWA), R-wave duration (RWD), and QRS area, were extracted from the ECG signal, resulting in time series that displayed a behavior similar to that of the respiration signals. EDR frequency was correlated with directly measured respiratory frequency, and averaged over all subjects. The peak-to-peak value of the EDR signals during the apnea event was compared to the average peak-to-peak of the sleep stage, containing the apnea

Automatic detection of slow-wave-sleep using heart rate variability

In this study, we used heart rate variability parameters to first characterize and then automatically detect slow-wave sleep (SWS). First, a wavelet transform was used to decompose equally sampled R-R interval series into their time-dependent spectral components: very low frequency (VLF) 0.005-0-04Hz, low frequency (LF) 0.04-0.15 Hz, and high frequency (HF) 0.15-0.45Hz. Then, the known decrease in LF power during SWS was confirmed and a linear relation between the average LF/HF balance throughout the night and the balance during SWS was found. Also, similar behaviour was found with the VLF power and the VLF/HF ratio. Finally, a decision algorithm with two criteria was defined using a training set of ECG recordings and applied to a test set. The results amounted to an 80% correct identification of SWS. The limitations of the study, as well as inherent differences between SWS definitions based on EEG and ECG, are discussed.

Estimation of autonomic response based on individually determined time axis

The analysis of the time-dependence of autonomic response requires: 1. A reliable procedure for the quantification of autonomic activity under nonsteady conditions, such as an algorithm for time-frequency decomposition (ex. SDA. Wigner-Ville, or others). 2. The choice of an adequate time scale for focusing on the data: (a) the regular, universal time scale, independent of the unsteady physiological conditions, or (b) a time axis defined by specific events related to an applied perturbation, as the indicators of specific experimental or physiological conditions, so that each individual is considered according to his own intrinsic time scale. The alignment of the various subjects according to their intrinsic time scale, reflecting their individual response mechanisms, may help to disclose a common pattern of autonomic function. Using an absolute time scale to align and average results for different subjects may obscure the underlying mechanisms. Several examples of autonomic challenges are presented, in which the use of an individual time scale contributes to unveil a typical response pattern: tilt test in vasovagal syncope, the autonomic effect of active standing on hypertension, and the autonomic response to acute hypoxia.

Detection of different recumbent body positions from the electrocardiogram

Changes in body position alter the relative angle between ECG electrodes and the mean electric axis of the heart. These changes influence the time interval during which the projection of the electric dipole, on any ECG lead, is positive (R-wave). In this study, measurements of R-wave duration (RWD) were used to identify changes in body position, and two of its uncorrelated features were used to classify each heartbeat into four basic groups relating to four body positions (supine, prone, left-side, right-side). Data were acquired from healthy volunteers during controlled condition experiments that included well-defined sequences of body positions and simultaneous recordings of ECG leads I, II and III. Results showed over 90% correct identifications ofbody position changes when using any of the three leads. Lead II had the best performance for theclassification of body position and correctly classified 80% of heartbeats. Classification did not improve for a combination of two leads. The technique can be used to reveal additional important clinical information and can be easily implemented, in a variety of applications where ECG is recorded, such as sleep studies, Holter recordings and ischaemia detection.

Selective windowed time-frequency analysis for the quantitative evaluation of non-stationary cardiovascular signals

A simple and efficient algorithm is developed for the investigation of non-stationary cardiovascular signals. It performs a DFT on windowed parts of the signal, according to the specific frequency and is referred to as Selective Discrete Fourier Transform Algorithm-SDA. It calculates the time-variant spectral components of the signal, displaying a 3-D time-dependent spectrum. The performance of the SDA is tested on simulated data and its capacity to detect the time evolution of the spectral components is proven. Then, the SDA is applied on real HR signal, during vagal maneuvers and transition from supine to upright position. It displays a clear ability to defect and quantitate ANS time-dependent changes. It allows one to analyze and establish the time evolution of the spectral components of the signal, not only during the steady state but also during transient changes.<<ETX>>