Abstract

Study objectives

This study investigated alterations in resting-state functional connectivity (RSFC) and hyperarousal biomarkers in patients with chronic insomnia disorder (CID), compared with good sleepers (GS). We also examined the relationships between altered RSFC and hyperarousal biomarkers.

Methods

Fifty patients with CID and fifty-two GS completed self-reporting questionnaires, and then underwent polysomnography and resting-state functional magnetic resonance imaging. We analyzed RSFC in the amygdala (AMG) and anterior insula (aINS), which are core regions of the salience network that are likely to be involved in hyperarousal. We also analyzed electroencephalography (EEG) relative beta power and heart rate variability (HRV) parameters (e.g., low and high frequency) during sleep. We then tested between-group differences in the RSFC and hyperarousal biomarkers; we examined correlations of RSFC with EEG beta power and HRV.

Results

Compared with GS, patients with CID showed more negative RSFC between the right AMG and left supramarginal gyrus (SMG), but less positive RSFC between the left aINS and bilateral lateral prefrontal cortex. The AMG-SMG RSFC was negatively correlated with EEG beta power in central regions (C3: r = −0.336, P = 0.012; C4: r = −0.314, P = 0.024).

Conclusions

Decreased RSFC between the AMG and SMG in patients with insomnia may reflect difficulty in cortical top-down regulation of the AMG, indicating daytime hyperarousal. Individuals who experience hyperarousal during the daytime may also exhibit cortical hyperarousal during sleep, as indicated by increased EEG beta power.

https://doi.org/10.1093/sleep/zsad205

Abstract

Various stimulation systems to modulate sleep structure and function have been introduced. However, studies on the time spent in sleep initiation (TSSI) are limited. This study proposes a closed-loop auditory stimulation (CLAS) to gradually modulate respiratory rhythm linked to the autonomic nervous system (ANS) activity directly associated with sleep. CLAS is continuously updated to reflect the individual’s current respiratory frequency and pattern. Six participants took naps on different days with and without CLAS. The average values of the TSSI are 14.00 ± 4.24 and 9.67 ± 5.31 min in the control and stimulation experiments (p < 0.03), respectively. Further, the values of respiratory instability and heart rate variability differ significantly between the control and stimulation experiments. Based on our findings, CLAS supports the individuals to gradually modulate their respiratory rhythms to have similar characteristics observed near sleep initiation, and the changed respiratory rhythms influence ANS activities, possibly influencing sleep initiation. Our approach aims to modulate the respiratory rhythm, which can be controlled intentionally. Therefore, this method can probably be used for sleep initiation and daytime applications.

https://doi.org/10.3390/s23146468 

Abstract

Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality. 

https://link.springer.com/article/10.1007/s13534-023-00305-8

Abstract

Among the various sleep modulation methods for improving sleep, three methods using noninvasive stimulation during sleep have been reviewed and summarized. The first method involves noninvasive direct brain stimulation to induce a current directly in the brain cortex. Electrically or magnetically applied stimulations trigger electrical events such as slow oscillations or sleep spindles, which can also be recorded by an electroencephalogram. The second method involves sensory stimulation during sleep, which provides stimulation through the sensory pathway to invoke equivalent brain activity like direct brain stimulation. Olfactory, vestibular, and auditory stimulation methods have been used, resulting in several sleep-modulating effects, which are characteristic and depend on the experimental paradigm. The third method is to modulate sleep by shifting the autonomic balance affecting sleep homeostasis. To strengthen parasympathetic dominance, stimulation was applied to decrease heart rate by synchronizing the heart rhythm. These noninvasive stimulation methods can strengthen slow-wave sleep, consolidate declarative or procedural memory, and modify sleep macrostructure. These stimulation methods provide evidence and possibility for sleep modulation in our daily life as an alternative method for the treatment of disturbed sleep and enhancing sleep quality and performance beyond the average level.

https://link.springer.com/article/10.1007/s13534-023-00298-4 

Abstract

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment. 

https://doi.org/10.3390/s23063116 

Abstract

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 res- piratory 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.

https://doi.org/10.1016/j.compbiomed.2021.104762 

Abstract

Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen’s kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson’s correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of. 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user’s location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment. 

10.1109/ACCESS.2021.3081747 

Abstract

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. 

10.1109/JBHI.2021.3072644 

Abstract

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. 

https://doi.org/10.1093/sleep/zsaa285 

Abstract

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. 

10.1109/JBHI.2020.2979168 

Abstract

STUDY OBJECTIVES

Idiopathic rapid eye movement sleep behavior disorder (iRBD), characterized by rapid eye movement sleep without atonia (RSWA) and dream-enactment behavior, has been suggested to be a predictor of α-synucleinopathies. Autonomic instability, represented by heart rate variability, is a common characteristic of both iRBD and α-synucleinopathies. Previous studies reported that RSWA was associated with autonomic dysfunction and was a possible predictor of phenoconversion. Therefore, we sought to compare heart rate variability between iRBD and control groups and explore the relationship between heart rate variability and RSWA in patients with iRBD.

METHODS

Nocturnal polysomnographic data on 47 patients (28 men, 19 women) diagnosed with iRBD based on video-polysomnography and 26 age-matched and sex-matched controls were reviewed. The first 5-minute epoch with a stable electrocardiogram lead II on video-polysomnography was selected from stage N2, wake, and rapid eye movement. For quantification of RSWA, tonic activity was analyzed from the submentalis electromyogram and phasic activity from the submentalis and bilateral anterior tibialis electromyogram channels.

RESULTS

Compared to the control group, the iRBD group showed significant reductions in the standard deviation of the R-R intervals, the root mean square of successive R-R interval differences, and high-frequency values. Quantified tonic activity was inversely correlated with normalized low-frequency values and low-frequency/high-frequency ratios and positively correlated with normalized high-frequency values.

CONCLUSIONS

This study implied decreased cardiac autonomic function in patients with iRBD, which showed parasympathetic predominance. Heart rate variability of the patients with iRBD in this study was associated with quantified tonic RSWA, which was previously reported to be a possible predictor of phenoconversion.

https://doi.org/10.5664/jcsm.8934 

Abstract

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. 

https://doi.org/10.3390/s19194136 

Abstract

Purpose

To determine the effect of obstructive sleep apnea (OSA) during rapid eye movement (REM) sleep on autonomic dysfunction using heart rate variability (HRV) analysis.

Methods

The medical records of adults who underwent nocturnal polysomnography at the Sleep and Chronobiology Center at Seoul National University Hospital were retrospectively reviewed. HRV parameters (mean RR interval, the standard deviation of all normal RR intervals [SDNN], square root of the mean squared differences of adjacent RR intervals [RMSSD], normalized low frequency [LF], normalized high frequency [HF], and the ratio of LF to HF [LF/HF]) were measured in 5-min electrocardiogram recordings obtained during W, N2, and R sleep stages. Comparisons were made among the control (apnea–hypopnea index (AHI < 15 and AHI during REM sleep (AHIREM) < 15, n = 27), REM-associated OSA (AHI < 15 and AHIREM ≥ 15, n = 27), and OSA (AHI ≥ 15, n = 27) groups. The groups were matched for age, sex, and body mass index.

Results

No significant differences were observed between the control and the REM-associated OSA groups for any of the HRV parameters. In contrast, compared with controls, the OSA group showed significantly lower normalized HF (p = 0.031) and higher LF/HF (p = 0.018) in stage W and a significantly shorter mean RR interval (p = 0.046) and lower RMSSD (p = 0.034) in stage N2.

Conclusions

Our findings suggest that OSA during REM sleep is not a major contributor to autonomic dysfunction.

https://doi.org/10.1007/s11325-018-01779-y 

Abstract

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. 

https://doi.org/10.3389/fphys.2019.00190 

Abstract

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. 

https://doi.org/10.1016/j.psychres.2018.11.021 

Abstract

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. 

https://doi.org/10.1016/j.compbiomed.2018.06.028 

Abstract

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. 

10.1109/TBME.2018.2819719 

Abstract

Objectives

Obstructive sleep apnea (OSA) is a major sleep disorder that causes insufficient sleep, which is linked with daytime fatigue and accidents. Long-term sleep monitoring can provide meaningful information for patients with OSA to prevent and manage their symptoms. Even though various methods have been proposed to objectively measure sleep in ambulatory environments, less reliable information was provided in comparison with standard polysomnography (PSG). Therefore, this paper proposes an algorithm for distinguishing wakefulness from sleep using a patch-type device, which is applicable for both healthy individuals and patients with OSA.

Methods

Electrocardiogram (ECG) and 3-axis accelerometer signals were gathered from the single device. Wakefulness was determined with six parallel methods based on information about movement and autonomic nervous activity. The performance evaluation was conducted with five-fold cross validation using the data from 15 subjects with a low respiratory disturbance index (RDI) and 10 subjects with high RDI. In addition, wakefulness information, including total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO), were extracted from the proposed algorithm and compared with those from PSG.

Results

According to epoch-by-epoch (30 s) analysis, the performance results of detecting wakefulness were an average Cohen's kappa of 0.60, accuracy of 91.24%, sensitivity of 64.12%, and specificity of 95.73%. Moreover, significant correlations were observed in TST, SE, SOL, and WASO between the proposed algorithm and PSG (p < 0.001).

Conclusions

Wakefulness-related information was successfully provided using data from the patch-type device. In addition, the performance results of the proposed algorithm for wakefulness detection were competitive with those from previous studies. Therefore, the proposed system could be an appropriate solution for long-term objective sleep monitoring in both healthy individuals and patients with OSA.

https://doi.org/10.1016/j.cmpb.2017.12.010 

Abstract

We propose an intelligent system that can recognize drowsiness during daily life with the use of EEG measurements in the ear canal in combination with conventional photoplethysmography (PPG) and electrocardiography (ECG). The physiological signals for classification by machine learning were measured during the sustained attention task of simulated driving. The features were sorted by their degree of importance using three types of ranking filters and the combined information. The effect of the feature size of the biological signals on machine learning was evaluated by determining the mean squared error. The classifications were conducted with various datasets and dataset lengths that were obtained from the same biological signals considering the transitional traits of drowsiness. The statistical measures of the performance of the classifications using machine learning indicated that the system based on the ear canal EEG data and the physiological attribute data was excellent. The feature selection process with the composite ranking algorithm using multiple ranking methods improved the classification performance. The nonlinear features were highly selective among the physiological attributes for the intelligent recognition of drowsiness. 

https://doi.org/10.1016/j.ins.2018.04.003 

Abstract

Sleep is an essential time for body recovery and healthy living. Therefore, sleep monitoring for health management is important. The gold-standard method for evaluating sleep is polysomnography (PSG), and physicians score the sleep stages using night PSG recording data. However, scoring sleep stages requires considerable time and labor. Hence, more accessible and efficient sleep-scoring methods are required. Because sleep stage information provides significant information for healthcare, studies of automatic sleep scoring have been conducted to overcome the limitations of PSG. This study reviews the progress and challenges of single- and multi-biosignalbased deep learning approaches to classify the sleep stages. In addition, non-contact sensor-based methods are reviewed for long-term monitoring at home. 

https://doi.org/10.5573/IEIESPC.2023.12.1.30

Abstract

Objective

This study was performed to determine the diagnostic cutoff value of quantified tonic and phasic rapid eye movement (REM) sleep without atonia (RSWA) automatically calculated from chin and limb muscle electromyograms (EMG) for diagnosis of REM sleep behavior disorder (RBD).

Methods

Nocturnal video polysomnographic data of 57 patients diagnosed with RBD and 29 age- and sex-matched controls were reviewed. Tonic activity was measured using submentalis EMG, and phasic activity was measured using submentalis and bilateral anterior tibialis EMG. The proportion of epochs with tonic and phasic activity during the entire REM sleep period was quantified using a self-developed automated algorithm.

Results

The RBD group showed significantly more tonic activity compared with the control group (28.87±36.92% vs. 12.94±31.69%, respectively, p<0.001). The diagnostic cutoff value of quantified submentalis tonic RSWA for RBD showing the best optimal sensitivity and specificity was 0.99% [sensitivity, 77.2%; specificity, 79.3%, area under the receiver operating characteristic curve (AUC), 0.76]. Cutoffs of phasic RSWA were 47.53% when assessed in the submentalis only (sensitivity, 1.8%; specificity, 100%; AUC, 0.46), 0.10% in the anterior tibialis (sensitivity, 66.7%; specificity, 55.2%; AUC, 0.55), and 0.10% in both the submentalis and anterior tibialis (sensitivity, 70.2%; specificity, 51.7%; AUC, 0.53).

Conclusion

This study provided evidence for the diagnosis of RBD using an automated method by assessing RSWA. Tonic activity in the submentalis muscle showed better sensitivity and specificity for diagnosis of RBD than did phasic activity.

https://doi.org/10.33069/cim.2020.0016 

Abstract

With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea?hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare. 

https://link.springer.com/article/10.1007%2Fs13534-018-0091-2

Abstract

Although many studies have analyzed the relationship between electrodermal activity (EDA) and sleep stages, a practical method for detecting sleep stage using EDA has not been suggested. The aim of this study was to develop an algorithm for real-time automatic detection of deep sleep using the EDA signal. Simultaneously with overnight polysomnography (PSG), continuous measurement of skin conductance on the fingers was performed for ten subjects. The morphometric characteristics in the fluctuations of EDA signal were employed to establish the quantitative criteria for determining deep sleep. The 30-sec epoch-by-epoch comparison between the deep sleep detected by our method and that reported from PSG exhibited an average sensitivity of 74.6%, an average specificity of 98.0%, and an average accuracy of 96.1%. This study may address the growing need for a reliable and simple measure for identifying sleep stage without a PSG. 

https://doi.org/10.9718/JBER.2015.36.5.204