Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)

Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea

This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy (94% for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e.g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing). © 2013 IEEE

Quantification of Periodic Breathing in Premature Infants

Periodic breathing (PB), regular cycles of short apneic pauses and breaths, is common in newborn infants. To characterize normal and potentially pathologic PB, we used our automated apnea detection system and developed a novel method for quantifying PB. We identified a preterm infant who died of sudden infant death syndrome (SIDS) and who, on review of her breathing pattern while in the neonatal intensive care unit (NICU), had exaggerated PB. We analyzed the chest impedance signal for short apneic pauses and developed a wavelet transform method to identify repetitive 10-40 second cycles of apnea/breathing. Clinical validation was performed to distinguish PB from apnea clusters and determine the wavelet coefficient cutoff having optimum diagnostic utility. We applied this method to analyze the chest impedance signals throughout the entire NICU stays of all 70 infants born at 32 weeks\u27 gestation admitted over a two-and-a-half year period. This group includes an infant who died of SIDS and her twin. For infants of 32 weeks\u27 gestation, the fraction of time spent in PB peaks 7-14 d after birth at 6.5%. During that time the infant that died of SIDS spent 40% of each day in PB and her twin spent 15% of each day in PB. This wavelet transform method allows quantification of normal and potentially pathologic PB in NICU patients

Clinical and Physiological Analysis of Very Long Apneas in Premature Infants

Apnea is common in premature infants, and in severe cases it may impair development. Data recorded during apnea events by hospital monitors at the University of Virginia Neonatal Intensive Care Unit (NICU) include EKG, chest impedance, and pulse oximetry signals. In previous work, an apnea detection algorithm was developed that filtered the cardiac artifact from the chest impedance signal to improve detection of apneas [1]. An unexpected result was the discovery that Very Long Apneas (VLAs) lasting more than 60 seconds are not rare. We use this findings in our research to provide new information about these apneas and to test a model describing the rate of decrease of blood oxygen in apneas of various lengths. We study 86 very long apneas, along with 285 shorter apneas (10 - 40 s duration), to analyze the properties of VLAs. We begin with a quantitative measure of the oxygen deficit or the heartbeat deficit resulting from the apnea, concluding that both are roughly proportional to the duration of the apnea. We observe that heart rate and oxygen saturation decrease much more slowly in a VLA than in a short apnea, and the initial oxygen saturation prior to VLAs is unusually high. This raises the question of whether babies are hyperventilating before a VLA. To answer this, we have analyzed respiration rates preceding apneas of various durations, and have shown that VLAs are associated with a significantly increased respiration rate immediately prior to the apnea. Lastly, we have used the theory provided by [2] to model the rate of decrease in oxygen saturation during individual apnea events. The resulting model confirms our observation that higher initial levels of oxygen saturation result in slower rates of decrease

Automatic sleep apnea detection and sleep classification using the ECG and the SpO2 signals

Dissertation for a Masters Degree in Computer and Electronic EngineeringThe present work describes the aspects to implement a system that can be used as a swift and accessible screening tool in patients whose complaints are compatible with OSAS (Obstructive Sleep Apnea Syndrome). This system only uses two signals, electrocardiogram (ECG) and the saturation of oxygen in arterial blood flow (SPO2). This system would be applied for the ambulatory automatic screening of OSAS, which currently are done in a Hospital environment, with a substantial waiting list. The system also would overcome the time consuming visual sleep scoring that contributes for the mentioned waiting list. We have developed a system that automatically detects OSAS based on the ECG and SpO2. However this system has to be paired up with another that detects the awake/sleep/REM periods (also based on the ECG), which is also part of this work. This last component has proved to produce results that are complex to classify,for which there is still a lack of research work. However we have described the necessary algorithms, and have used state-of-the-art signal processing tools, such as wavelets

Improving detection of apneic events by learning from examples and treatment of missing data

The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-474-9-213[Abstract] This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best performance regardless the imputation method used

Cardiac autonomic activity during sleep in high-altitude resident children compared with lowland residents

Study Objectives We aimed to characterize heart-rate variability (HRV) during sleep in Andean children native to high altitude (HA) compared with age, gender, and genetic ancestry-similar low-altitude (LA) children. We hypothesized that the hypoxic burden of sleep at HA could induce variation in HRV. As children have otherwise healthy cardiovascular systems, such alterations could provide early markers of later cardiovascular disease. Methods Twenty-six LA (14F) and 18 HA (8F) children underwent a single night of attended polysomnography. Sleep parameters and HRV indices were measured. Linear mixed models were used to assess HRV differences across sleep stage and altitude group. Results All children showed marked fluctuations in HRV parameters across sleep stages, with higher vagal activity during nonrapid eye movement sleep and greater variability of the heart rate during rapid eye movement (REM). Moreover, HA children showed higher very low-frequency HRV in REM sleep and, after adjusting for heart rate, higher low-to-high frequency ratio in REM sleep compared with children living at lower altitude. Conclusions We confirmed previous findings of a stage-dependent modulation of HRV in Andean children living at both HA and LA. Moreover, we showed subtle alteration of HRV in sleep in HA children, with intriguing differences in the very low-frequency domain during REM sleep. Whether these differences are the results of an adaptation to high-altitude living, or an indirect effect of differences in oxyhemoglobin saturation remains unclear, and further research is required to address these questions