Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Study Objectives: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Methods: Power spectra from PSGs of 8716 participants, including from the MrOS Sleep Study and the Sleep Heart Health Study, were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models.

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST.

Remote Estimation of Peripheral Oxygen Saturation and Pulse Rate From Facial Analysis Using a Smartphone Camera.

Vital sign monitoring is an invaluable tool for healthcare professionals, both in the hospital and at home. Traditional measurement devices provide accurate readings but require physical contact with the patient which often is unsuitable, furthermore contact-based devices have been reported to fail by loosing contact due to movement as severe events occur, therefore, a contactless method is necessary.We hypothesize that, in ideal scenarios, it is possible to estimate both SpO and pulse rate using only facial video recorded with a smartphone's front-facing camera.

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy.

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is caused by motor disinhibition during REM sleep and is a strong early predictor of Parkinson's disease. However, screening questionnaires for iRBD lack specificity due to other sleep disorders that mimic the symptoms. Nocturnal wrist actigraphy has shown promise in detecting iRBD by measuring sleep-related motor activity, but it relies on sleep diary-defined sleep periods, which are not always available.

The association between vital signs abnormalities during postanaesthesia care unit stay and deterioration in the general ward following major abdominal cancer surgery assessed by continuous wireless monitoring.

Vital signs abnormalities in the post-anaesthesia care unit (PACU) may identify patients at risk of severe postoperative complications in the general ward, but are sparsely investigated by continuous monitoring. We aimed to assess if the severity of vital signs abnormalities in the PACU was correlated to the duration of severe vital signs abnormalities and serious adverse events (SAEs) in the general ward. Prospective cohort study.

SViT: A Spectral Vision Transformer for the Detection of REM Sleep Behavior Disorder.

REM sleep behavior disorder (RBD) is a parasomnia with dream enactment and presence of REM sleep without atonia (RSWA). RBD diagnosed manually via polysomnography (PSG) scoring, which is time intensive. Isolated RBD (iRBD) is also associated with a high probability of conversion to Parkinson's disease.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator.

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients' circadian rhythm.

Deviations in continuously monitored electrodermal activity before severe clinical complications: a clinical prospective observational explorative cohort study.

Monitoring of high-risk patients in hospital wards is crucial in identifying and preventing clinical deterioration. Sympathetic nervous system activity measured continuously and non-invasively by Electrodermal activity (EDA) may relate to complications, but the clinical use remains untested. The aim of this study was to explore associations between deviations of EDA and subsequent serious adverse events (SAE).

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis.

Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models).

Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy.

Wearable wireless electrocardiographic (ECG) monitoring is well-proven for arrythmia detection, but ischemia detection accuracy is not well-described. We aimed to assess the agreement of ST-segment deviation from single- versus 12-lead ECG and their accuracy for the detection of reversible ischemia. Bias and limits of agreement (LoA) were calculated between maximum deviations in ST segments from single- and 12-lead ECG during Rb PET-myocardial cardiac stress scintigraphy.

Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19.

Background: Patients admitted to the emergency care setting with COVID-19-infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements.

Automatic scoring of drug-induced sleep endoscopy for obstructive sleep apnea using deep learning.

Background: Treatment of obstructive sleep apnea is crucial for long term health and reduced economic burden. For those considered for surgery, drug-induced sleep endoscopy (DISE) is a method to characterize location and pattern of sleep-related upper airway collapse. According to the VOTE classification system, four upper airway sites of collapse are characterized: velum (V), oropharynx (O), tongue (T), and epiglottis (E).

Agreement between standard and continuous wireless vital sign measurements after major abdominal surgery: a clinical comparison study.

. Continuous wireless monitoring outside the post-anesthesia or intensive care units may enable early detection of patient deterioration, but good accuracy of measurements is required. We aimed to assess the agreement between vital signs recorded by standard and novel wireless devices in postoperative patients.

Continuous wireless pre- and postoperative vital sign monitoring reveal new, severe desaturations after vascular surgery.

Objectives: Postoperative deviating physiologic values (vital signs) may represent postoperative stress or emerging complications. But they can also reflect chronic preoperative values. Distinguishing between the two circumstances may influence the utility of using vital signs in patient monitoring.

Ambulatory Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Combining Actigraphy and Questionnaire.

Background: Isolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used.

Prediction of Serious Adverse Events from Nighttime Vital Signs Values.

The period directly following surgery is critical for patients as they are at risk of infections and other types of complications, often summarized as severe adverse events (SAE). We hypothesize that impending complications might alter the circadian rhythm and, therefore, be detectable during the night before. We propose a SMOTE-enhanced XGBoost prediction model that classifies nighttime vital signs depending on whether they precede a serious adverse event or come from a patient that does not have a complication at all, based on data from 450 postoperative patients.

Detection of Cheyne-Stokes Breathing using a transformer-based neural network.

Annotation of sleep disordered breathing, including Cheyne-Stokes Breathing (CSB), is an expensive and time-consuming process for the clinician. To solve the problem, this paper presents a deep learning-based algorithm for automatic sample-wise detection of CSB in nocturnal polysomnographic (PSG) recordings. 523 PSG recordings were retrieved from four different sleep cohorts and subsequently scored for CSB by three certified sleep technicians.

End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.

Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic method relies on manual scoring of polysomnograms (PSGs), a procedure that is time and effort intensive, subject to interscorer variability, and requires high level of expertise. Here, we present an automatic and interpretable diagnostic tool for RBD that analyzes PSGs using end-to-end deep neural networks.

Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models.

This project assessed the use of multivariate auto-regressive (MAR) models to create forecasts of continuous vital signs in hospitalized patients. A total of 20 hours continuous (1/60Hz) heart rate and respiration rate from eight postoperative patients, where used to fit a centered MAR model for forecasting in windows of 15 minutes. The model was fitted using Markov Chain Monte Carlo sampling, and the model was evaluated on data from five additional patients.

Age estimation from sleep studies using deep learning predicts life expectancy.

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90.

A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography.

Wrist-worn consumer sleep technologies (CST) that contain accelerometers (ACC) and photoplethysmography (PPG) are increasingly common and hold great potential to function as out-of-clinic (OOC) sleep monitoring systems. However, very few validation studies exist because raw data from CSTs are rarely made accessible for external use. We present a deep neural network (DNN) with a strong temporal core, inspired by U-Net, that can process multivariate time series inputs with different dimensionality to predict sleep stages (wake, light-, deep-, and REM sleep) using ACC and PPG signals from nocturnal recordings.

Transcutaneous blood gas monitoring and tissue perfusion during common femoral thromboendarterectomy.

Background: Improving tissue perfusion can improve clinical outcomes in surgical patients, where monitoring may aid clinicians in detecting adverse conditions and guide interventions. Transcutaneous monitoring (TCM) of oxygen (tcpO) and carbon dioxide (tcpCO) is a well-proven technology and could potentially serve as a measure of local circulation, perfusion and metabolism, but the clinical use is not thoroughly explored. The purpose of this proof-of-concept study was to investigate whether TCM of blood gasses could detect changes in perfusion during major vascular surgery.

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitoring is primarily based on intermittent observation of vital signs and the early warning scores (EWS). The drawback is lack of time series dynamics and correlations among vital signs.

Clinical impact of vital sign abnormalities in patients admitted with acute exacerbation of chronic obstructive pulmonary disease: an observational study using continuous wireless monitoring.

Early detection of abnormal vital signs is critical for timely management of acute hospitalised patients and continuous monitoring may improve this. We aimed to assess the association between preceding vital sign abnormalities and serious adverse events (SAE) in patients hospitalised with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Two hundred patients' vital signs were wirelessly and continuously monitored with peripheral oxygen saturation, heart rate, and respiratory rate during the first 4 days after admission for AECOPD.