Slow-wave sleep estimation on a load-cell-installed bed: a non-constrained method

Effect of closed-loop vibration stimulation on sleep quality for poor sleepers

Introduction

Recent studies have investigated the autonomic modulation method using closed-loop vibration stimulation (CLVS) as a novel strategy for enhancing sleep quality. This study aimed to explore the effects of CLVS on sleep quality, autonomic regulation, and brain activity in individuals with poor sleep quality.

Methods

Twenty-seven participants with poor sleep quality (Pittsburgh sleep quality index >5) underwent two experimental sessions using polysomnography and a questionnaire, one with CLVS (STIM) and the other without (SHAM).

Results

Sleep macrostructure analysis first showed that CLVS significantly reduced the total time, proportion, and average duration of waking after sleep onset. These beneficial effects were paralleled by significantly increased self-reported sleep quality. Moreover, there was a significant increase in the normalized high-frequency (nHF) and electroencephalography relative powers of delta activity during N3 sleep under STIM. Additionally, coherence analysis between nHF and delta activity revealed strengthened coupling between cortical and cardiac oscillations.

Discussion

This study demonstrated that CLVS significantly improves sleep quality in individuals with poor sleep quality by enhancing both subjective and objective measures. These findings suggest that CLVS has the potential to be a practical, noninvasive tool for enhancing sleep quality in individuals with sleep disturbances, offering an effective alternative to pharmacological treatments.

Amygdala resting-state functional connectivity alterations in patients with chronic insomnia disorder: correlation with electroencephalography beta power during sleep

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 52 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 amygdala (R.AMG) and left supramarginal gyrus (L.SMG), but less positive RSFC between the left aINS and bilateral lateral prefrontal cortex. The R.AMG-L.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 R.AMG and L.SMG in patients with insomnia may reflect the 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.

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Age-dependent cardiorespiratory directional coupling in wake-resting state

Objective.Cooperation in the cardiorespiratory system helps maintain internal stability. Various types of system interactions have been investigated; however, the characteristics of the interactions have mostly been studied using data collected in well-defined physiological states, such as sleep. Furthermore, most analyses provided general information about the interaction, making it difficult to quantify how the systems influenced one another.Approach.Cardiorespiratory directional coupling was investigated in different age groups (20 young and 19 elderly subjects) in a wake-resting state. The directionality index (DI) was calculated using instantaneous phases from the heartbeat interval and respiratory signal to provide information about the strength and direction of interaction between the systems. Statistical analysis was performed between the groups on the DI and independent measures of directionality (nc_r: influence from cardiac system to respiratory system, and nc_c: influence from the respiratory system to the cardiac system).Main results.The values of DI were -0.52 and -0.17 in the young and elderly groups, respectively (p< 0.001). Furthermore, the values of nc_rand nc_cwere found to be significantly different between the groups (p< 0.001), respectively.Significance.Changes in both directions between the systems influence different aspects of cardiorespiratory coupling between the groups. This observation could be linked to different levels of autonomic modulation associated with ageing. Our approach could aid in quantitatively tracking and comprehending how systems interact in response to physiological and environmental changes. It could also be used to understand how abnormal interaction characteristics influence physiological system dysfunctions and disorders.

Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study

Thermal comfort during sleep is essential for both sleep quality and human health while sleeping. There are currently few effective contactless methods for detecting the sleep thermal comfort at any time of day or night. In this paper, a vision-based detection approach for human thermal comfort while sleeping was proposed, which is intended to avoid overcooling/overheating supply, meet the thermal comfort needs of human sleep, and improve human sleep quality and health. Based on 438 valid questionnaire surveys, 10 types of thermal comfort sleep postures were summarized. By using a large number of data captured, a fundamental framework of detection algorithm was constructed to detect human sleeping postures, and corresponding weighting model was established. A total of 2.65 million frames of posture data in natural sleep status were collected, and thermal comfort-related sleep postures dataset was created. Finally, the robustness and effectiveness of the proposed algorithm were validated. The validation results show that the sleeping posture and human skeleton keypoints can be used for estimating sleeping thermal comfort, and the the quilt coverage area can be fused to improve the detection accuracy.

Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning

Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.

Heart rate estimation from ballistocardiographic signals using deep learning

Objective.Ballistocardiography (BCG) is an unobtrusive approach for cost-effective and patient-friendly health monitoring. In this work, deep learning methods are used for heart rate estimation from BCG signals and are compared against five digital signal processing methods found in literature.Approach.The models are evaluated on a dataset featuring BCG recordings from 42 patients, acquired with a pneumatic system. Several different deep learning architectures, including convolutional, recurrent and a combination of both are investigated. Besides model performance, we are also concerned about model size and specifically investigate less complex and smaller networks.Main results.Deep learning models outperform traditional methods by a large margin. Across 14 patients in a held-out testing set, an architecture with stacked convolutional and recurrent layers achieves an average mean absolute error (MAE) of 2.07 beat min^-1, whereas the best-performing traditional method reaches 4.24 beat min^-1. Besides smaller errors, deep learning models show more consistent performance across different patients, indicating the ability to better deal with inter-patient variability, a prevalent issue in BCG analysis. In addition, we develop a smaller version of the best-performing architecture, that only features 8283 parameters, yet still achieves an average MAE of 2.32 beat min^-1on the testing set.Significance.This is the first study that applies and compares different deep learning architectures to heart rate estimation from bed-based BCG signals. Compared to signal processing algorithms, deep learning models show dramatically smaller errors and more consistent results across different individuals. The results show that using smaller models instead of excessively large ones can lead to sufficient performance for specific biosignal processing applications. Additionally, we investigate the use of fully convolutional networks for 1D signal processing, which is rarely applied in literature.

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.

Association of heart rate variability with REM sleep without atonia in idiopathic REM sleep behavior disorder

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.

A Unified Approach to Wearable Ballistocardiogram Gating and Wave Localization

OBJECTIVE

Toward the ultimate goal of cuff-less blood pressure (BP) trend tracking via pulse transit time (PTT) using wearable ballistocardiogram (BCG) signals, we present a unified approach to the gating of wearable BCG and the localization of wearable BCG waves.

METHODS

We present a unified approach to localize wearable BCG waves suited to various gating and localization reference signals. Our approach gates individual wearable BCG beats and identifies candidate waves in each wearable BCG beat using a fiducial point in a reference signal, and exploits a pre-specified probability distribution of the time interval between the BCG wave and the fiducial point in the reference signal to accurately localize the wave in each wearable BCG beat. We tested the validity of our approach using experimental data collected from 17 healthy volunteers.

RESULTS

We showed that our approach could localize the J wave in the wearable wrist BCG accurately with both the electrocardiogram (ECG) and the wearable wrist photoplethysmogram (PPG) signals as reference, and that the wrist BCG-PPG PTT thus derived exhibited high correlation to BP.

CONCLUSION

We demonstrated the proof-of-concept of a unified approach to localize wearable BCG waves suited to various gating and localization reference signals compatible with wearable measurement.

SIGNIFICANCE

Prior work using the BCG itself or the ECG to gate the BCG beats and localize the waves to compute PTT are not ideally suited to the wearable BCG. Our approach may foster the development of cuff-less BP monitoring technologies based on the wearable BCG.

A new respiratory monitor system for four-dimensional computed tomography by measuring the pressure change on the back of body

OBJECTIVE

A novel respiratory monitoring method based on the periodical pressure change on the patient's back was proposed and assessed by applying to four-dimensional CT (4DCT) scanning.

METHODS

A pressure-based respiratory monitoring system is developed and validated by comparing to real-time position management (RPM) system. The pressure change and the RPM signal are compared with phase differences and correlations calculated. The 4DCT images are reconstructed by these two signals. Internal and skin artifacts due to mismatch between CT slices and respiratory phases are evaluated.

RESULTS

The pressure and RPM signals shows strong consistency (R = 0.68±0.19 (1SD)). The time shift is 0.26 ± 0.51 (1SD) s and the difference of breath cycle is 0.02 ± 0.17 (1SD) s. The quality of 4DCT images reconstructed by two signals is similar. For both methods, the number of patients with artifacts is eight and the maximum magnitudes of artifacts are 20 mm (internal) and 10 mm (skin). The average magnitudes are 8.8 mm (pressure) and 8.2 mm (RPM) for internal artifacts, and 5.2 mm (pressure) and 4.6 mm (RPM) for skin artifacts. The mean square gray value difference shows no significant difference (p = 0.52).

CONCLUSION

The pressure signal provides qualified results for respiratory monitoring in 4DCT scanning, demonstrating its potential application for respiration monitoring in radiotherapy.

ADVANCES IN KNOWLEDGE

Pressure change on the back of body is a novel and promising method to monitor respiration in radiotherapy, which may improve treatment comfort and provide more information about respiration and body movement.

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.

Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals

We present a new algorithm for peak detection in ballistocardiographic (BCG) signals and use it for heart rate estimation. Systolic complexes of the BCG signal are enhanced and coarse heart beat locations estimated. Ejection waves I, J and K are detected simultaneously around coarse locations, only using detection of local maxima and weighted summation of peak heights. Due to a lack of reference BCG annotations, the algorithm's performance is assessed by using the detected peaks for heart rate estimation. On a dataset acquired with a pneumatic BCG system, we evaluate the heart rate estimation performance and compare the introduced algorithm against other methods found in literature. The dataset is gathered from 42 patients in a clinical environment and provides low-quality signals taken from a realistic scenario. With a mean absolute percentage error of 2.58 % at 65 % coverage, the presented method is on par with the best-performing state-of-the-art algorithms investigated. Limits of agreement (5th/95th percentiles) in a comparison with ECG-based heart rate measurements lie within P₅ = -3.63 and P₉₅ = 5.78 beat/min.

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.

Smart technologies toward sleep monitoring at home

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.

A rapid approach to assess cardiac contractility by ballistocardiogram and electrocardiogram

In this paper, we propose a rapid assessment on cardiac contractility by using the time interval between the I wave of ballistocardiogram (BCG) and the R wave of electrocardiogram (ECG) which is referred to as the RI interval. The whole work can be divided into two parts. First, the correlation between the RI interval and the ejection fraction (EF), which is a clinical index to assess systolic performance, was computed. For 39 subjects, the correlation coefficient is -0.54 (p<0.001). Moreover, RI intervals of heart failure (HF) patients and healthy subjects were measured, and a significant difference was found among different New York Heart Association (NYHA) classes and the healthy group. Second, the beat-to-beat correlation analysis between the RI interval and the pre-ejection period (PEP), which is a parameter of systolic time interval to evaluate the cardiac contractility, was calculated. For 4578 heart beats across eight healthy subjects, the correlation coefficient is 0.85 (p<0.001). As a conclusion, these results indicate that the RI interval can be used as a noninvasive assessment of cardiac contractility.

A Pilot Study of an Unobtrusive Bed-Based Sleep Quality Monitor for Severely Disabled Autistic Children

The link between daytime performance and sleep quality for severely disabled autistic children is not entirely understood. This paper presents nighttime data collected from a child with severe disabilities during a three-night pilot study conducted at Heartspring, Wichita, KS, using a bed-based system capable of unobtrusively tracking parameters for sleep quality assessment. The 'average sample correlation coefficient signal-to-noise ratio' is compared for ballistocardiograms acquired using four electromechanical film sensors versus four load cell sensors. The "best" signal or sensing modality depends on the subject's sleeping position. These results affirm the importance of a bed system that is robust in its ability to track sleep quality accurately regardless of sleeping position.

Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device

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.

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.

Effect of Missing Inter-Beat Interval Data on Heart Rate Variability Analysis Using Wrist-Worn Wearables

Most of the wrist-worn devices on the market provide a continuous heart rate measurement function using photoplethysmography, but have not yet provided a function to measure the continuous heart rate variability (HRV) using beat-to-beat pulse interval. The reason for such is the difficulty of measuring a continuous pulse interval during movement using a wearable device because of the nature of photoplethysmography, which is susceptible to motion noise. This study investigated the effect of missing heart beat interval data on the HRV analysis in cases where pulse interval cannot be measured because of movement noise. First, we performed simulations by randomly removing data from the RR interval of the electrocardiogram measured from 39 subjects and observed the changes of the relative and normalized errors for the HRV parameters according to the total length of the missing heart beat interval data. Second, we measured the pulse interval from 20 subjects using a wrist-worn device for 24 h and observed the error value for the missing pulse interval data caused by the movement during actual daily life. The experimental results showed that mean NN and RMSSD were the most robust for the missing heart beat interval data among all the parameters in the time and frequency domains. Most of the pulse interval data could not be obtained during daily life. In other words, the sample number was too small for spectral analysis because of the long missing duration. Therefore, the frequency domain parameters often could not be calculated, except for the sleep state with little motion. The errors of the HRV parameters were proportional to the missing data duration in the presence of missing heart beat interval data. Based on the results of this study, the maximum missing duration for acceptable errors for each parameter is recommended for use when the HRV analysis is performed on a wrist-worn device.

Noncontact Pressure-Based Sleep/Wake Discrimination

Poor sleep is increasingly being recognized as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics, which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists, and an unrepresentative sleeping environment. A home-based noncontact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an undermattress sleep monitoring system for noncontact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimized feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity, and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation dataset (71.9%, 87.9%, and 56% and 77.5%, 98%, and 57% is reported for younger and older subjects, respectively). These results compare favorably with similar research, however this system provides an ambient alternative suitable for long-term continuous sleep monitoring, particularly among vulnerable populations.

Bed-based instrumentation for unobtrusive sleep quality assessment in severely disabled autistic children

The relationship between sleep quality and daytime wellness and performance in severely disabled, autistic children is not well understood. While polysomnography and, more recently, actigraphy serve as means to obtain sleep assessment data from neurotypical children and adults, these techniques are not well-suited to severely autistic children. This paper presents recent progress on a bed sensor suite that can unobtrusively track physiological and behavioral parameters used to assess sleep quality. Electromechanical films and load cells provide data that yield heart rate, respiration rate, center of position, in-and-out-of-bed activity, and general movement, while thermocouples are used to detect bed-wetting events.

Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R-R Intervals

We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R-R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night. The automatic algorithm was evaluated based on a fivefold cross validation using data from 21 healthy subjects and 24 patients with OSA. An epoch-by-epoch (30 s) analysis showed that the overall Cohen's kappa, accuracy, sensitivity, and specificity of our method were 0.56, 89.97%, 68.71%, and 93.75%, respectively. SWS-related information, including SWS duration (min) and percentage (%), were also calculated. A significant correlation in these parameters was found between automatic and polysomnography scorings. Compared with similar methods, the proposed algorithm convincingly discriminated SWS from non-SWS. The simple method using only R-R intervals has the potential to be utilized in mobile and wearable devices that can easily measure this information. Moreover, when combined with other sleep staging methods, the proposed method is expected to be applicable to long-term sleep monitoring at home and ambulatory environments.

REM sleep estimation based on autonomic dynamics using R-R intervals

OBJECTIVE

We developed an automatic algorithm to determine rapid eye movement (REM) sleep on the basis of the autonomic activities reflected in heart rate variations.

APPROACH

The heart rate variability (HRV) parameters were calculated using the R-R intervals from an electrocardiogram (ECG). A major autonomic variation associated with the sleep cycle was extracted from a combination of the obtained parameters. REM sleep was determined with an adaptive threshold applied to the acquired feature. The algorithm was optimized with the data from 26 healthy subjects and obstructive sleep apnea (OSA) patients and was validated with data from a separate group of 25 healthy and OSA subjects.

MAIN RESULTS

According to an epoch-by-epoch (30 s) analysis, the average of Cohen's kappa and the accuracy were respectively 0.63 and 87% for the training set and 0.61 and 87% for the validation set. In addition, the REM sleep-related information extracted from the results of the proposed method revealed a significant correlation with those from polysomnography (PSG).

SIGNIFICANCE

The current algorithm only using R-R intervals can be applied to mobile and wearable devices that acquire heart-rate-related signals; therefore, it is appropriate for sleep monitoring in the home and ambulatory environments. Further, long-term sleep monitoring could provide useful information to clinicians and patients for the diagnosis and treatments of sleep-related disorders and individual health care.

Unconstrained Measurement of Respiration Motions of Chest and Abdomen Using a Tactile Sensor Sheet in Supine Position on Bed

The management of health through daily monitoring of respiration is of major importance for early diagnosis to prevent respiratory and circulatory diseases. Such daily health monitoring is possible only if the monitoring system is physically and psychologically noninvasive. However, current unconstrained measurement methods cannot distinguish chest and abdominal movements in diagnosing sleep apnea. In this study, a flexible and stretchable tactile sensor sheet was developed to measure the static body pressure of a subject who lies on it and measure the pressure fluctuations induced by respiration or respiratory efforts. The results were compared with the measurements by band sensors that are widely used for measuring chest and abdominal movements in clinic. It was demonstrated that the sensor sheet can distinguish chest and abdominal movements in a supine position. The reasons why the pressure fluctuations measured by the sensor sheet are antiphase with the outputs of band sensors are discussed using a simple dynamic model.

J peak extraction from non-standard ballistocardiography data: a preliminary study

In recent years, several advanced algorithms based on clustering, multi-method or data fusion approaches have been proposed to estimate heartbeat intervals from non-standard ballistocardiography (BCG) data. These advanced algorithms generally have higher computational complexity than J-peak based algorithms. This fact motivated us to study the problem of extracting J peaks from non-standard BCG data, because if this extraction can be realized, then a low-complexity J-peak based algorithm can be used to fast estimate heartbeat intervals from non-standard BCG data. We found that most of the energy in J peaks is contained in a relatively narrow frequency band, called J-peak band, and that the heartbeat harmonics outside the J-peak band can cause the non-standard BCG waveform. According to these findings, a FIR linear phase filter with the J-peak band as its pass-band is proposed. The experimental result demonstrates the ability of the proposed filter to extract J peaks from non-standard BCG data.

Unobtrusive sleep state measurements in preterm infants - A review

Sleep is important for the development of preterm infants. During sleep, neural connections are formed and the development of brain regions is triggered. In general, various rudimentary sleep states can be identified in the preterm infant, namely active sleep (AS), quiet sleep (QS) and intermediate sleep (IS). As the infant develops, sleep states change in length and organization, with these changes as important indicators of brain development. As a result, several methods have been deployed to distinguish between the different preterm infant sleep states, among which polysomnography (PSG) is the most frequently used. However, this method is limited by the use of adhesive electrodes or patches that are attached to the body by numerous cables that can disturb sleep. Given the importance of sleep, this review explores more unobtrusive methods that can identify sleep states without disturbing the infant. To this end, after a brief introduction to preterm sleep states, an analysis of the physiological characteristics associated with the different sleep states is provided and various methods of measuring these physiological characteristics are explored. Finally, the advantages and disadvantages of each of these methods are evaluated and recommendations for neonatal sleep monitoring proposed.

Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors

Ballistocardiographs (BCGs), which record the mechanical activity of the heart, have been a subject of interest for several years because of their advantages in providing unobtrusive physiological measurements. BCGs could also be useful for monitoring the biological signals of infants without the need for physical confinement. In this study, we describe a physiological signal monitoring bed based on load cells and assess an algorithm to extract the heart rate and breathing rate from the measured load-cell signals. Four infants participated in a total of 13 experiments. As a reference signal, electrocardiogram and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiration information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiration information were found to have average performance errors of 2.55% and 2.66%, respectively. The experimental results verify the positive feasibility of BCG-based measurements in infants.

Ballistocardiogram of baby during sleep

Ballistocardiogram (BCG), which displays the mechanical activity of heart, has been a subject of interest for several years due to its advantages in taking unobtrusive physiological measurements. In the field of sleep science, researchers actively study sleep architecture and clinically apply various sleep-related conditions through BCG-derived biological information such as the heartbeat, respiration and body movements of subjects. However, most of these studies have involved only adults. This area of research may be even more important with babies to monitor their biological signals without confinement. For this reason, we developed a physiological signal monitoring bed for baby by using a load cell. Heartbeat and respiration information was assessed with average respective performance errors of 1.53% and 2.53% compared to commercial equipment. The results showed the possibility of applying BCG technology to baby. Therefore, we expect that BCG-derived signals can be extensively applied to analyze sleep architecture and clinical applications in baby as they are with adults.

Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques

Monitoring vital signs through unobtrusive means is a goal which has attracted a lot of attention in the past decade. This review provides a systematic and comprehensive review over the current state of the field of ambient and unobtrusive cardiorespiratory monitoring. To this end, nine different sensing modalities which have been in the focus of current research activities are covered: capacitive electrocardiography, seismo- and ballistocardiography, reflective photoplethysmography (PPG) and PPG imaging, thermography, methods relying on laser or radar for distance-based measurements, video motion analysis, as well as methods using high-frequency electromagnetic fields. Current trends in these subfields are reviewed. Moreover, we systematically analyze similarities and differences between these methods with respect to the physiological and physical effects they sense as well as the resulting implications. Finally, future research trends for the field as a whole are identified.

Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

We present a method for measuring beat-to-beat heart rate from ballistocardiograms acquired with force sensors. First, a model for the heartbeat shape is adaptively inferred from the signal using hierarchical clustering. Then, beat-to-beat intervals are detected by finding positions where the heartbeat shape best fits the signal. The method was validated with overnight recordings from 46 subjects in varying setups (sleep clinic, home, single bed, double bed, two sensor types). The mean beat-to-beat interval error was 13 ms and on an average 54% of the beat-to-beat intervals were detected. The method is part of a home-use e-health system for an unobtrusive sleep measurement.

Estimation of body postures on bed using unconstrained ECG measurements

We developed and tested a system for estimating body postures on a bed using unconstrained measurements of electrocardiogram (ECG) signals using 12 capacitively coupled electrodes and a conductive textile sheet. Thirteen healthy subjects participated in the experiment. After detecting the channels in contact with the body among the 12 electrodes, the features were extracted on the basis of the morphology of the QRS (Q wave, R wave, and S wave of ECG) complex using three main steps. The features were applied to linear discriminant analysis, support vector machines with linear and radial basis function (RBF) kernels, and artificial neural networks (one and two layers), respectively. SVM with RBF kernel had the highest performance with an accuracy of 98.4% for estimation of four body postures on the bed: supine, right lateral, prone, and left lateral. Overall, although ECG data were obtained from few sensors in an unconstrained manner, the performance was better than the results that have been reported to date. The developed system and algorithm can be applied to the obstructive apnea detection and analyses of sleep quality or sleep stages, as well as body posture detection for the management of bedsores.

Unobtrusive online monitoring of sleep at home

We describe an online sleep monitoring service, based on unobtrusive ballistocardiography (BCG) measurement in an ordinary bed. The novelty of the system is that the sleep tracking web application is based on measurements from a fully unobtrusive sensor. The BCG signal is measured with a piezoelectric film sensor under the mattress topper, and sent to the web server for analysis. Heart rate and respiratory variation, activity, sleep stages, and stress reactions are inferred based on the signal. The sleep information is presented to the user along with measurements of the sleeping environment (temperature, noise, luminosity) and user-logged tags (e.g. stress, alcohol, exercise). The approach is designed for long-term use at home, allowing users to follow the development of their sleep over months and years. The service has also a medical use, as sleep disorder patients can be measured for long periods before and after interventions.

Sleep-quality assessment from full night audio recordings of sleep apnea patients

In this work, a novel system (method) for sleep quality analysis is proposed. Its purpose is to assist an alternative non-contact method for detecting and diagnosing sleep related disorders based on acoustic signal processing. In this work, audio signals of 145 patients with obstructive sleep apnea were recorded (more than 1000 hours) in a sleep laboratory and analyzed. The method is based on the assumption that during sleep the respiratory efforts are more periodically patterned and consistent relative to a waking state; furthermore, the sound intensity of those efforts is higher, making the pattern more noticeable relative to the background noise level. The system was trained on 50 subjects and validated on 95 subjects. The system accuracy for detecting sleep/wake state is 82.1% (epoch by epoch), resulting in 3.9% error (difference) in detecting sleep latency, 11.4% error in estimating total sleep time, and 11.4% error in estimating sleep efficiency.

Unobtrusive classification of sleep and wakefulness using load cells under the bed

Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patient's own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the "gold-standard" sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.

Validation of heart rate extraction through an iPhone accelerometer

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.

Adaptive beat-to-beat heart rate estimation in ballistocardiograms

A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.

Classification of lying position using load cells under the bed

Individuals who suffer from acid reflux at night, who snore chronically, or who have sleep apnea are frequently encouraged to sleep in a particular lying position. Side sleeping decreases the frequency and severity of obstructive respiratory events (e.g. apnea and hypopnea) in patients with positional sleep apnea. It has been suggested that individuals with Gastroesophageal Reflux Disease sleep on their left sides in order to help minimize symptoms. In this paper, we present a method of predicting the position of an individual lying on the bed using load cells placed under each of the bed supports. Our results suggest that load cells utilized in this manner could be successfully implemented into a system that tracks or helps train individuals to sleep in a particular lying position.