Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these can be measured using advanced unobtrusive techniques that are currently available, promising application for personal and continuous home monitoring. This paper describes a methodology classifying wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light deep 30 s epoch basis. A total of 142 features were...

Abstract Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based monitoring. Current methods however are limited in their ability take into account long-term architectural patterns. A long short-term memory (LSTM) network is proposed as a solution model cardiac architecture information validated on comprehensive data set (292 participants, 584...

To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements an accelerometer, polysomnography (PSG) and actigraphy. Using wrist-worn PPG to analyze accelerometer measure movements, stages statistics were automatically computed overnight recordings. Sleep–wake, 4-class (wake/N1 + N2/N3/REM) 3-class (wake/NREM/REM) classifiers trained 135 simultaneously recorded PSG recordings 101 healthy participants...

Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better disorder screening and health monitoring. However, PPG is rarely included in large studies with gold-standard annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks challenging. In this work a recurrent network first trained data set electrocardiogram (ECG) (292 participants, 584 recordings) to perform 4-class stage...

Objective. Evaluate a method for the estimation of nocturnal systolic blood pressure (SBP) dip from 24 h trends using wrist-worn photoplethysmography (PPG) sensor and deep neural network in free-living individuals, comparing to traditional machine learning non-machine baselines. Approach. A PPG was worn by 106 healthy individuals 226 d during which 5111 reference values (BP) were obtained with ambulatory BP monitor matched data. Features based on heart rate variability pulse morphology...

Abstract Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected disordered patients. Methods We applied designed for automatic long short-term memory recurrent neural networks to model architecture. The classifier uses 132 HRV features computed from electrocardiography activity counts accelerometry. retrained our two public datasets containing both healthy sleepers then...

Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms achieve the single EEG signal. classification performance obtained using six different signals various feature sets compared kappa statistic which very become popular in A variable duration segment (or epoch) decide stage also analyzed. Spectral-domain, time-domain, linear,...

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The velocity of the propagating arterial pulse wave (pulse velocity, PWV) has been proposed as an unobtrusive and possibly continuous surrogate measure systolic blood pressure (SBP). PWV is derived from arrival time at a peripheral location, most often finger. Reported performances were not yet accurate enough for clinical application but good in other settings. However, finger PPG ideal location home setting it obstructs hand movement can suffer vasomotion orthostatic changes. In this paper...

In this work we investigate the use of behavior feasibility to adapt and personalize lifestyle-targeting recommender systems for prevention treatment hypertension. Based on survey data (N=300) model feasibiliy 63 behaviors through a Rasch model, describing engagement in as function behavior's difficulty person's ability. We formulate two feasibility-tailored recommendation strategies that utilize model. The maximization strategy aims at maximizing probability by proposing very feasible while...

Abstract Objective The maturation of neural network-based techniques in combination with the availability large sleep datasets has increased interest alternative methods monitoring. For unobtrusive staging, most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived ECG-data. practical application these is even more when ways obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating staging...

Abstract Drought has a significant influence on both in the environment and area of agriculture, particularly farming. In this scenario, Adaptive Neuro-Fuzzy Inference System (ANFIS), one hybrid artificial neural networks, is primarily used study to anticipate drought. The Coimbatore district's monthly precipitation values for previous 39 years are study. First, as district depends North-East Monsoon, SPI estimated at 3-month scale using values. Second, several ANFIS forecasting models built...

Automated sleep stage classification using heart-rate variability is an active field of research. In this work limitations the current state-of-the-art are addressed through use deep learning techniques and their efficacy demonstrated. First, a temporal model proposed for inference stages from electrocardiography long- short-term (LSTM) classifier it shown that outperforms previous approaches which were often limited to non-temporal or Markovian classifiers on comprehensive benchmark data...

One of the numerous cloud-based services is e-health system, which stores and shares patient medical data among healthcare professionals patients. It operates mostly through computer or electronic systems cloud technologies. The semi-trusted third-party supplier (the cloud) information. Consequently, security has emerged as primary worry because no unauthorized individual ought to be able obtain data. To help advance field system security, this paper purposes give a brief overview features...

Abstract Introduction Typically, neurological signals are not recorded in home sleep apnea testing (HSAT) and thus standard scoring is applicable. The respiratory event index calculated using total recording time rather than (TST) resulting a risk of underestimating severity. objective the study was to evaluate if artificial intelligence approaches can provide based on cardiorespiratory (CReSS) with reasonable accuracy. Methods Supervised deep learning for trained 472 tested 116...

The development of the Internet Things (IoT), which enables communication between people, things, data, and virtual platforms in environment, is a result exponential rise information technology (IT). Recently, numerous decision support systems medical industry have been offered via IoT cloud-based e-health services. Owing to developments IoT-enabled gadgets sensing devices. Chronic diseases are often considered as major source concern threat public health on global scale. kidneys one body’s...