Changes in physiological network connectivity of body system in narcolepsy during REM sleep.

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.

Attention-Based LSTM for Non-Contact Sleep Stage Classification Using IR-UWB Radar.

Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Sixty-five young (30.0 ± 8.6 yrs.) and healthy volunteers underwent nocturnal PSG and IR-UWB radar measurement simultaneously; From 51 recordings, 26 were used for training, 8 for validation, and 17 for testing. Sixteen features including movement-, respiration-, and heart rate variability-related indices were extracted from the raw IR-UWB signals in each 30-s epoch. Sleep stage classification performances of Attention Bi-LSTM model with optimized hyperparameters were evaluated and compared with those of conventional LSTM networks for same test dataset. In the results, we achieved an accuracy of 82.6 ± 6.7% and a Cohen's kappa coefficient of 0.73 ± 0.11 in the classification of wake stage, REM sleep, light (N1+N2) sleep, and deep (N3) sleep which is significantly higher than the conventional LSTM networks (p < 0.01). Moreover, the classification performances were higher than those reported in comparative studies, demonstrating the effectiveness of the attention mechanism coupled with bi-LSTM networks for the sleep staging using cardiorespiratory signals.

Weak closed-loop vibrational stimulation improves the depth of slow-wave sleep and declarative memory consolidation.

Sleep is a unique behavioral state that affects body functions and memory. Although previous studies suggested stimulation methods to enhance sleep, a new method is required that is practical for long-term and unconstrained use by people. In this study, we used a novel closed-loop vibration stimulation method that delivers a stimulus in interaction with the intrinsic heart rhythm and examined the effects of stimulation on sleep and memory. Twelve volunteers participated in the experiment and each underwent one adaptation night and two experimental conditions-a stimulation condition (STIM) and a no-stimulation condition (SHAM). The heart rate variability analysis showed a significant increase in the normalized high frequency and the normalized low frequency significantly decreased under the STIM during the slow-wave sleep (SWS) stage. Furthermore, the synchronization ratio between the heartbeat and the stimulus significantly increased under the STIM in the SWS stage. From the electroencephalogram (EEG) spectral analysis, EEG relative powers of slow-wave activity and theta frequency bands showed a significant increase during the STIM in the SWS stage. Additionally, memory retention significantly increased under the STIM compared to the SHAM. These findings suggest that the closed-loop stimulation improves the SWS-stage depth and memory retention, and further provides a new technique for sleep enhancement.

Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor.

Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold standard method for assessing sleep, it is obtrusive and difficult to apply for long-term sleep monitoring. Further, because human experts manually classify sleep stages, it is time-consuming and exhibits inter-rater variability. Therefore, this article proposes a long short-term memory (LSTM) model for automatic sleep stage scoring using a polyvinylidene fluoride (PVDF) film sensor that can provide unconstrained long-term physiological monitoring. Signals were recorded using a PVDF sensor during PSG. From 60 recordings, 30 were used for training, 10 for validation, and 20 for testing. Sixteen parameters, including movement, respiration-related, and heart rate variability, were extracted from the recorded signals and then normalized. From the selected LSTM architecture, four sleep stage classification performances were evaluated for a test dataset and the results were compared with those of conventional machine learning methods. According to epoch-by-epoch (30 s) analysis, the classification performance for the four sleep stages had an average accuracy of 73.9% and a Cohen's kappa coefficient of 0.55. When compared with other machine learning methods, the proposed method achieved the highest classification performance. The use of LSTM networks with the PVDF film sensor has potential for facilitating automatic sleep scoring, and it can be applied for long-term sleep monitoring at home.

Effect of Closed-Loop Vibration Stimulation on Heart Rhythm during Naps.

Sleep plays a primary function for health and sustains physical and cognitive performance. Although various stimulation systems for enhancing sleep have been developed, they are difficult to use on a long-term basis. This paper proposes a novel stimulation system and confirms its feasibility for sleep. Specifically, in this study, a closed-loop vibration stimulation system that detects the heart rate (HR) and applies -n% stimulus beats per minute (BPM) computed on the basis of the previous 5 min of HR data was developed. Ten subjects participated in the evaluation experiment, in which they took a nap for approximately 90 min. The experiment comprised one baseline and three stimulation conditions. HR variability analysis showed that the normalized low frequency (LF) and LF/high frequency (HF) parameters significantly decreased compared to the baseline condition, while the normalized HF parameter significantly increased under the -3% stimulation condition. In addition, the HR density around the stimulus BPM significantly increased under the -3% stimulation condition. The results confirm that the proposed stimulation system could influence heart rhythm and stabilize the autonomic nervous system. This study thus provides a new stimulation approach to enhance the quality of sleep and has the potential for enhancing health levels through sleep manipulation.

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.

Heart rate variability changes in major depressive disorder during sleep: Fractal index correlates with BDI score during REM sleep.

We investigated the relationship between autonomic nervous system activity during each sleep stage and the severity of depressive symptoms in patients with major depressive disorder (MDD) and healthy control subjects. Thirty patients with MDD and thirty healthy control subjects matched for sex, age, and body mass index completed standard overnight polysomnography. Depression severity was assessed using the Beck Depression Inventory (BDI). Time- and frequency-domain, and fractal HRV parameters were derived from 5-min electrocardiogram segments during light sleep, deep sleep, rapid eye movement (REM) sleep, and the pre- and post-sleep wake periods. Detrended fluctuation analysis (DFA) alpha-1 values during REM sleep were significantly higher in patients with MDD than in control subjects, and a significant correlation existed between DFA alpha-1 and BDI score in all subjects. DFA alpha-1 was the strongest predictor for the BDI score, along with REM density as a covariate. This study found that compared with controls, patients with MDD show reduced complexity in heart rate during REM sleep, which may represent lower cardiovascular adaptability in these patients, and could lead to cardiac disease. Moreover, DFA alpha-1 values measured during REM sleep may be useful as an indicator for the diagnosis and monitoring of depression.

Sleep-Dependent Directional Coupling of Cardiorespiratory System in Patients With Obstructive Sleep Apnea.

OBJECTIVE: Cardiorespiratory interactions have been widely investigated in different physiological states and conditions. Various types of coupling characteristics have been observed in the cardiorespiratory system; however, it is difficult to identify and quantify details of their interaction. In this study, we investigate directional coupling of the cardiorespiratory system in different physiological states (sleep stages) and conditions, i.e., severity of obstructive sleep apnea (OSA). METHODS: Directionality analysis is performed using the evolution map approach with heartbeats acquired from electrocardiogram and abdominal respiratory effort measured from the polysomnographic data of 39 healthy individuals and 24 mild, 21 moderate, and 23 severe patients with OSA. The mean phase coherence is used to confirm the weak and strong coupling of cardiorespiratory system. RESULTS: We find that unidirectional coupling from the respiratory to the cardiac system increases during wakefulness (average value of -0.61) and rapid eye movement sleep (-0.55). Furthermore, unidirectional coupling between the two systems significantly decreases during light (-0.52) and deep sleep, which is further decreased in deep sleep (-0.46), approaching bidirectional coupling. In addition, unidirectional coupling from the respiratory to the cardiac system also significantly increases according to the severity of OSA. CONCLUSION: These coupling characteristics in different states and conditions are believed to be linked with autonomic nervous modulation. SIGNIFICANCE: Our approach could provide an opportunity to understand how integrated systems cooperate for physiological functions under internal and external environmental changes, and how abnormality in one physiological system could develop to increase the risk of other systemic dysfunctions and/or disorders.

Real-time apnea-hypopnea event detection during sleep by convolutional neural networks.

Sleep apnea-hypopnea event detection has been widely studied using various biosignals and algorithms. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they require hand-engineered feature extraction and selection processes. In this paper, we propose a new approach for real-time apnea-hypopnea event detection using convolutional neural networks and a single-channel nasal pressure signal. From 179 polysomnographic recordings, 50 were used for training, 25 for validation, and 104 for testing. Nasal pressure signals were adaptively normalized, and then segmented by sliding a 10-s window at 1-s intervals. The convolutional neural networks were trained with the data, which consisted of class-balanced segments, and were then tested to evaluate their event detection performance. According to a segment-by-segment analysis, the proposed method exhibited performance results with a Cohen's kappa coefficient of 0.82, a sensitivity of 81.1%, a specificity of 98.5%, and an accuracy of 96.6%. In addition, the Pearson's correlation coefficient between estimated apnea-hypopnea index (AHI) and reference AHI was 0.99, and the average accuracy of sleep apnea and hypopnea syndrome (SAHS) diagnosis was 94.9% for AHI cutoff values of ≥5, 15, and 30 events/h. Our approach could potentially be used as a supportive method to reduce event detection time in sleep laboratories. In addition, it can be applied to screen SAHS severity before polysomnography.