Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea

Cardiac and respiratory rhythms reveal transient phases of phase-locking which were proposed to be an important aspect of cardiorespiratory interaction. The aim of this study was to quantify cardio-respiratory phase-locking in obstructive sleep apnea (OSA). We investigated overnight polysomnography data of 248 subjects with suspected OSA. Cardiorespiratory phase-coupling was computed from the R-R intervals of body surface ECG and respiratory rate, calculated from abdominal and thoracic sensors, using Hilbert transform. A significant reduction in phase-coupling was observed in patients with severe OSA compared to patients with no or mild OSA. Cardiorespiratory phase-coupling was also associated with sleep stages and was significantly reduced during rapid-eye-movement (REM) sleep compared to slow-wave (SW) sleep. There was, however, no effect of age and BMI on phase coupling. Our study suggests that the assessment of cardiorespiratory phase coupling may be used as an ECG based screening tool for determining the severity of OSA

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)

Wearable Sleep Technology in Clinical and Research Settings

The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine

Human Heart Rhythms Synchronize While Co-sleeping

Human physiological systems have a major role in maintenance of internal stability. Previous studies have found that these systems are regulated by various types of interactions associated with physiological homeostasis. However, whether there is any interaction between these systems in different individuals is not well-understood. The aim of this research was to determine whether or not there is any interaction between the physiological systems of independent individuals in an environment where they are connected with one another. We investigated the heart rhythms of co-sleeping individuals and found evidence that in co-sleepers, not only do independent heart rhythms appear in the same relative phase for prolonged periods, but also that their occurrence has a bidirectional causal relationship. Under controlled experimental conditions, this finding may be attributed to weak cardiac vibration delivered from one individual to the other via a mechanical bed connection. Our experimental approach could help in understanding how sharing behaviors or social relationships between individuals are associated with interactions of physiological systems

Changes in Physiological Network Connectivity of Body System in Narcolepsy during REM Sleep: 렘 수면 중 기면 환자의 인체 시스템 내 생리학적 네트워크 연결성 변화

학위논문(석사) -- 서울대학교대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2022.2. 박광석.연구 배경: 기면증은 병리학적 증상을 수반하는 수면 질환의 하나로, 야간에 충분한 수면을 취했음에도 주간의 과도한 졸림증, 무기력증의 증상을 나타낸다. 기면증은 두 가지의 유형이 있으며 이들은 탈력발작을 동반한 1유형 기면증과 탈력발작을 동반하지 않는 2유형 기면증으로 구별된다. 1유형 기면증의 진단 생체 지표로서 히포크레틴이라는 신경 물질이 존재하지만, 그에 반하여 2유형 기면증은 적절한 생체 지표가 부재하여 2유형 기면증의 기면 증상 및 인과관계의 확인에 한계를 지니고 있다. 이에 기반하여, 본 연구는 2유형 기면증의 새로운 생체 지표의 탐색을 목표로 하며 이를 위해 인체의 시스템적 연결망의 분석을 진행하였다. 연구 방법: 본 연구는 30명의 참여자 (15명의 2유형 기면증 환자, 15명의 정상 대조군)를 대상으로, 시간 지연 안정성의 방법을 통해 시간적 정보에 기반하여 여러 생체신호들의 관계에 대한 분석을 진행하였다. 각 참여자의 야간 수면다원 검사로부터 얻은 9개 생체신호들 (뇌파, 심장, 호흡, 근육과 안구의 움직임으로 부터의 신호)의 연결성 네트워크에 대한 정량적 분석을 진행하였으며, 특히 수면 단계에 따른 네트워크 연결성의 차이가 두 그룹 사이에 어떠한 영향력을 미치며 이들이 기면증과 정상군을 구별하는 잠재적 생체 지표로서 사용될 수 있을지에 대한 분석에 중점을 두었다. 그룹 간의 차이에 대한 인과관계의 조사와 함께 생체 지표의 분류 성능의 확인을 위한 서포트 벡터 머신 기법을 적용한 조사도 함께 진행하였다. 연구 결과: 렘 수면에서, 기면 환자군은 정상 대조군에 비교하여 더 많은 네트워크의 연결을 보였다 (기면 환자 연결 수: 24.47 ± 2.87, 대조군 연결 수: 21.34 ± 3.49; p = 0.022). 이러한 차이는 여러 연결의 요소들 중 움직임과 관련된 기관과 심장 활동에서 유의미하게 나타난 것을 확인할 수 있었으며, 네트워크의 연결 개수와 유의미한 차이를 보이는 생체신호 요소의 정보를 이용한 서포트 벡터 머신 기반 분류 성능은 0.93의 민감도, 특이도, 정확도를 각각 나타내었다. 결 론: 본 연구는 시간 지연 안정성에 기반한 네트워크 연결성이 2유형 기면증을 대조군과 분류하는 데에 있어 유용한 생체지표로 이용될 수 있음을 보이며, 나아가 인체의 시스템적 네트워크에 대한 분석을 통해 차이에 대한 인과관계 분석 및 정량적 접근이 가능함을 보여준다.Background: Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body’s systemic networks. Method: Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine. Result: In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively. Conclusion: Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.Chapter 1. Introduction 1 1.1. Narcolepsy 1 1.2. Physiological interactions in body system 3 1.3. Connectivity with time delay stability 5 1.4. Dissertation Outline 7 Chapter 2. Material and Methods 9 2.1. Participants 9 2.2. PSG recording and data 12 2.3. Data processing 14 2.4. Time delay cross-correleation 17 2.5. TDS methods 20 2.6. Threshold tuning 22 2.7. Test-retest reproducibility 25 2.8. Brain and peripheral connections 26 2.9. Effect of brain-brain connections according to brain areas 27 2.10. Feature significance analysis 28 2.11. Network directionality with correlation 29 2.12. Verifications of network connectivity as classifier 30 2.13. Classification with support vector machine 31 Chater 3. Results and Discussion 32 3.1. Results 32 3.1.1. Network connections between narcolepsy and control groups 32 3.1.2. Test-retest analysis for reproducibility 35 3.1.3. Significant feature identification 36 3.1.4. Effect of brain-brain connections according to brain areas 39 3.1.5. Network directionality with correlation 44 3.1.6. Performance comparison between unimodal biosignal and connectivity 46 3.1.7. Classification performance with SVM 49 3.2. Discussion 51 3.2.1. Differences between patients with narcolepsy and healthy controls 51 3.2.2. Analysis of nervous system with HRV 52 3.2.3. Causalities in network connections 55 3.2.4. Effect of brain-brain connections 58 3.2.5. Network connectivity as a biomarker and prospective utility 59 Limitations 61 References 63 국문초록 72석

The effects of circadian rhythm disruption towards metabolic stress and mental health: a review

This review aims to present an overview of current research findings on the possible effects of circadian rhythm (CR) disruption towards metabolic stress and mental health. CR can be described as an internal biological clock that regulates our body functions, based on our sleep/wake cycle. Any time that our normal 24-hour circadian rhythm is altered or interrupted, it will have physiological and psychological impacts. However, in today’s demanding working world, most of us are working defying the normal conditions without realising the significant drawbacks of it. Therefore, this review summarises the findings from several researches on the physiological (metabolic stress) and psychological (cognitive functioning and mental health) impacts of the CR disruption in order to assist people to have a holistic view on the effects of CR to our mind and body. Evidences that linked these aspects to health circumstances of shift workers have also been highlighted

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed