Increasing accuracy of pulse arrival time estimation in low frequency recordings.

Objective.Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz.Approach.The method applies template matching to leverage the random nature of sampling time and expected change in the PAT.Main results.The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively.Significance.This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings.

Rationale and design of the BECA project: Smartwatch-based activation of the chain of survival for out-of-hospital cardiac arrest.

Aim: Out-of-hospital cardiac arrest is a major health problem, and the overall survival rate is low (4.6%-16.4%). The initiation of the current chain of survival depends on the presence of a witness of the cardiac arrest, which is not present in 29.7%-63.4% of the cases. Furthermore, a delay in starting this chain is common in witnessed out-of-hospital cardiac arrest. This project aims to reduce morbidity and mortality due to out-of-hospital cardiac arrest by developing a smartwatch-based solution to expedite the chain of survival in the case of (un)witnessed out-of-hospital cardiac arrest. Methods: Within the 'Beating Cardiac Arrest' project, we aim to develop a demonstrator product that detects out-of-hospital cardiac arrest using photoplethysmography and accelerometer analysis, and autonomously alerts emergency medical services. A target group study will be performed to determine who benefits the most from this product. Furthermore, several clinical studies will be conducted to capture or simulate data on out-of-hospital cardiac arrest cases, as to develop detection algorithms and validate their diagnostic performance. For this, the product will be worn by patients at high risk for out-of-hospital cardiac arrest, by volunteers who will temporarily interrupt blood flow in their arm by inflating a blood pressure cuff, and by patients who undergo cardiac electrophysiologic and implantable cardioverter defibrillator testing procedures. Moreover, studies on psychosocial and ethical acceptability will be conducted, consisting of surveys, focus groups, and interviews. These studies will focus on end-user preferences and needs, to ensure that important individual and societal values are respected in the design process.

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study.

BACKGROUND: Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. OBJECTIVE: The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. METHODS: In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor's ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient's wrist. The agreement between the HR reported by the patient's monitor and the HR extracted from the OHRM's PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. RESULTS: A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was -0.14 bpm (LoA between -3.08 bpm and 2.79 bpm) and -0.19% (LoA between -5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was -0.11 bpm (LoA between -2.79 bpm and 2.59 bpm) and -0.15% (LoA between -3.92% and 3.64%). CONCLUSIONS: This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of -5 bpm to 5 bpm or -10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients.

Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring.

INTRODUCTION: Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. METHODS AND ANALYSIS: We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm. ETHICS AND DISSEMINATION: The study was reviewed by the medical ethical committee of the Maxima Medical Center (Eindhoven, the Netherlands, File no: N16.074). All subjects provide informed consent before participation.The SOMNIA database is built to facilitate future research in sleep medicine. Data from the completed SOMNIA database will be made available for collaboration with researchers outside the institute.

Estimating blood pressure trends and the nocturnal dip from photoplethysmography.

OBJECTIVE: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24 h blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. APPROACH: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 d during which 5111 reference values for blood pressure (BP) were obtained with a 24 h ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long- and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip. MAIN RESULTS: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12 [Formula: see text] 2.20 [Formula: see text] mmHg and a correlation of 0.69 [Formula: see text]. This dip was derived from trend estimates of BP which had an RMSE of 8.22 [Formula: see text] 1.49 mmHg for systolic and 6.55 [Formula: see text] 1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip. SIGNIFICANCE: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24 h measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

Conceptions of sleep experience: a layman perspective.

OBJECTIVE: To date, there is little information on how lay people understand and discuss sleep in the context of daily life. Efforts to conceptualize sleep quality have been largely driven by clinical considerations of sleep disorders. As such, they are not necessarily of how normal sleepers without clinical expertise conceptualize sleep quality. A phenomenological approach was taken to understand the essence of the sleep experience and the concepts held by lay people without sleep disorders. A sentence completion questionnaire was developed and administered to a quota sample of 64 respondents who were selected aiming for sufficient representation of different gender, ages, and education levels. RESULTS: Significant sentences and meaningful units were derived inductively, resulting in a classification of nine categories. The major facets of sleep experience of lay people were 'daytime functioning', 'interruptions during the night' and 'before bed state'. This implies that the experienced sleep quality is not only depending on the progress of the night. These results can guide future research to provide suitable psychometric measures for normal sleepers, as well as the design of sleep data visualization applications in the context of health self-monitoring.

Detection of Nocturnal Slow Wave Sleep Based on Cardiorespiratory Activity in Healthy Adults.

Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This study aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features, and thus, enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large dataset of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and tenfold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8%) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min, which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.

Effects of Between- and Within-Subject Variability on Autonomic Cardiorespiratory Activity during Sleep and Their Limitations on Sleep Staging: A Multilevel Analysis.

Autonomic cardiorespiratory activity changes across sleep stages. However, it is unknown to what extent it is affected by between- and within-subject variability during sleep. As it is hypothesized that the variability is caused by differences in subject demographics (age, gender, and body mass index), time, and physiology, we quantified these effects and investigated how they limit reliable cardiorespiratory-based sleep staging. Six representative parameters obtained from 165 overnight heartbeat and respiration recordings were analyzed. Multilevel models were used to evaluate the effects evoked by differences in sleep stages, demographics, time, and physiology between and within subjects. Results show that the between- and within-subject effects were found to be significant for each parameter. When adjusted by sleep stages, the effects in physiology between and within subjects explained more than 80% of total variance but the time and demographic effects explained less. If these effects are corrected, profound improvements in sleep staging can be observed. These results indicate that the differences in subject demographics, time, and physiology present significant effects on cardiorespiratory activity during sleep. The primary effects come from the physiological variability between and within subjects, markedly limiting the sleep staging performance. Efforts to diminish these effects will be the main challenge.

Sleep stage classification with ECG and respiratory effort.

Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that are currently available, promising the application for personal and continuous home sleep monitoring. This paper describes a methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis. A total of 142 features were extracted from electrocardiogram and thoracic respiratory effort measured with respiratory inductance plethysmography. To improve the quality of these features, subject-specific Z-score normalization and spline smoothing were used to reduce between-subject and within-subject variability. A modified sequential forward selection feature selector procedure was applied, yielding 80 features while preventing the introduction of bias in the estimation of cross-validation performance. PSG data from 48 healthy adults were used to validate our methods. Using a linear discriminant classifier and a ten-fold cross-validation, we achieved a Cohen's kappa coefficient of 0.49 and an accuracy of 69% in the classification of wake, REM, light, and deep sleep. These values increased to kappa = 0.56 and accuracy = 80% when the classification problem was reduced to three classes, wake, REM sleep, and NREM sleep.

Improving sleep/wake detection via boundary adaptation for respiratory spectral features.

In previous work, respiratory spectral features have been successfully used for sleep/wake detection. They are usually extracted from several frequency bands. However, these traditional bands with fixed frequency boundaries might not be the most appropriate to optimize the sleep and wake separation. This is caused by the between-subject variability in physiology, or more specifically, in respiration during sleep. Since the optimal boundaries may relate to mean respiratory frequency over the entire night. Therefore, we propose to adapt these boundaries for each subject in terms of his/her mean respiratory frequency. The adaptive boundaries were considered as those being able to maximize the separation between sleep and wake states by means of their mean power spectral density (PSD) curves overnight. Linear regression models were used to address the association between the adaptive boundaries and mean respiratory frequency based on training data. This was then in turn used to estimate the adaptive boundaries of each test subject. Experiments were conducted on the data from 15 healthy subjects using a linear discriminant classifier with a leave-one-subject-out cross-validation. We reveal that the spectral boundary adaptation can help improve the performance of sleep/wake detection when actigraphy is absent.

Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging.

Polysomnography (PSG) has been extensively studied for sleep staging, where sleep stages are usually classified as wake, rapid-eye-movement (REM) sleep, or non-REM (NREM) sleep (including light and deep sleep). Respiratory information has been proven to correlate with autonomic nervous activity that is related to sleep stages. For example, it is known that the breathing rate and amplitude during NREM sleep, in particular during deep sleep, are steadier and more regular compared to periods of wakefulness that can be influenced by body movements, conscious control, or other external factors. However, the respiratory morphology has not been well investigated across sleep stages. We thus explore the dissimilarity of respiratory effort with respect to its signal waveform or morphology. The dissimilarity measure is computed between two respiratory effort signal segments with the same number of consecutive breaths using a uniform scaling distance. To capture the property of signal morphological dissimilarity, we propose a novel window-based feature in a framework of sleep staging. Experiments were conducted with a data set of 48 healthy subjects using a linear discriminant classifier and a ten-fold cross validation. It is revealed that this feature can help discriminate between sleep stages, but with an exception of separating wake and REM sleep. When combining the new feature with 26 existing respiratory features, we achieved a Cohen's Kappa coefficient of 0.48 for 3-stage classification (wake, REM sleep and NREM sleep) and of 0.41 for 4-stage classification (wake, REM sleep, light sleep and deep sleep), which outperform the results obtained without using this new feature.

Automatic detection of overnight deep sleep based on heart rate variability: a preliminary study.

This preliminary study investigated the use of cardiac information or more specifically, heart rate variability (HRV), for automatic deep sleep detection throughout the night. The HRV data can be derived from cardiac signals, which were obtained from polysomnography (PSG) recordings. In total 42 features were extracted from the HRV data of 15 single-night PSG recordings (from 15 healthy subjects) for each 30-s epoch, used to perform epoch-by-epoch classification of deep sleep and non-deep sleep (including wake state and all the other sleep stages except deep sleep). To reduce variation of cardiac physiology between subjects, we normalized each feature per subject using a simple Z-score normalization method by subtracting the mean and dividing by the standard deviation of the feature values. A correlation-based feature selection (CFS) method was employed to select informative features as well as removing feature redundancy and a linear discriminant (LD) classifier was applied for deep and non-deep sleep classification. Results show that the use of Z-score normalization can significantly improve the classification performance. A Cohen's Kappa coefficient of 0.42 and an overall accuracy of 81.3% based on a leave-one-subject-out cross-validation were achieved.

Sleep and wake classification with actigraphy and respiratory effort using dynamic warping.

This paper proposes the use of dynamic warping (DW) methods for improving automatic sleep and wake classification using actigraphy and respiratory effort. DW is an algorithm that finds an optimal nonlinear alignment between two series allowing scaling and shifting. It is widely used to quantify (dis)similarity between two series. To compare the respiratory effort between sleep and wake states by means of (dis)similarity, we constructed two novel features based on DW. For a given epoch of a respiratory effort recording, the features search for the optimally aligned epoch within the same recording in time and frequency domain. This is expected to yield a high (or low) similarity score when this epoch is sleep (or wake). Since the comparison occurs throughout the entire-night recording of a subject, it may reduce the effects of within- and between-subject variations of the respiratory effort, and thus help discriminate between sleep and wake states. The DW-based features were evaluated using a linear discriminant classifier on a dataset of 15 healthy subjects. Results show that the DW-based features can provide a Cohen's Kappa coefficient of agreement κ = 0.59 which is significantly higher than the existing respiratory-based features and is comparable to actigraphy. After combining the actigraphy and the DW-based features, the classifier achieved a κ of 0.66 and an overall accuracy of 95.7%, outperforming an earlier actigraphy- and respiratory-based feature set ( κ = 0.62). The results are also comparable with those obtained using an actigraphy- and cardiorespiratory-based feature set but have the important advantage that they do not require an ECG signal to be recorded.

Respiration amplitude analysis for REM and NREM sleep classification.

In previous work, single-night polysomnography recordings (PSG) of respiratory effort and electrocardiogram (ECG) signals combined with actigraphy were used to classify sleep and wake states. In this study, we aim at classifying rapid-eye-movement (REM) and non-REM (NREM) sleep states. Besides the existing features used for sleep and wake classification, we propose a set of new features based on respiration amplitude. This choice is motivated by the observation that the breathing pattern has a more regular amplitude during NREM sleep than during REM sleep. Experiments were conducted with a data set of 14 healthy subjects using a linear discriminant (LD) classifier. Leave-one-subject-out cross-validations show that adding the new features into the existing feature set results in an increase in Cohen's Kappa coefficient to a value of κ = 0.59 (overall accuracy of 87.6%) compared to that obtained without using these features (κ of 0.54 and overall accuracy of 86.4%). In addition, we compared the results to those reported in some other studies with different features and signal modalities.