Attention on Sleep Stage Specific Characteristics DOI
Iris A. M. Huijben, Sebastiaan Overeem, Merel M. van Gilst

и другие.

Опубликована: Июль 15, 2024

Manual sleep stage classification relies on visual inspection of 30-second windows comprising multi-sensor measurements The ability neural networks to model complex relations has made them a popular, faster, alternative. However, it often remains unclear which parts the data predominantly contributed model's decision. This is especially ambiguous in staging, where coarse labeling per may assign mixtures class-specific features single class. To boost transparency deep classifiers, we propose dynamic discrete attention module that actively selects subset input space aligned with class label. can be combined typical network, and additionally serve as data-driven tool discover specific polysomnography data. We validate method synthetic patient observe only small from window required retain accurate classification, mechanism boosts performance. Analysis masks, moreover, shows clear adaptive channel selection.

Язык: Английский

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography DOI Creative Commons
Maretha Bester,

M. J. Almario Escorcia,

Pedro Fonseca

и другие.

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Ноя. 30, 2023

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of derived from wrist-worn photoplethysmography (PPG) measurements may allow for early detection deteriorations in health. However, even though a plethora these features-specifically, describing heart rate variability (HRV) morphology PPG waveform (morphological features)-exist literature, it is unclear which be valuable As an initial step towards clarity, we compute comprehensive sets HRV morphological nighttime measurements. From these, using logistic regression stepwise forward feature elimination, identify that best differentiate healthy pregnant women non-pregnant women, since likely capture physiological adaptations necessary sustaining pregnancy. Overall, were more discriminating than (area under receiver operating characteristics curve 0.825 0.74, respectively), with systolic pulse wave deterioration being most single feature, followed by mean (HR). Additionally, stratified analysis sleep stages found calculated only periods deep enhanced differences two groups. In conclusion, postulate addition features, also useful health suggest specific included future research concerning

Язык: Английский

Процитировано

3
Maternal cardiorespiratory coupling: differences between pregnant and nonpregnant women are further amplified by sleep-stage stratification DOI Creative Commons
Maretha Bester,

Giulia Perciballi,

Pedro Fonseca

и другие.

Journal of Applied Physiology, Год журнала: 2023, Номер 135(5), С. 1199 - 1212

Опубликована: Сен. 28, 2023

We compare CRC, i.e., the reciprocal interaction between cardiac and respiratory systems, healthy pregnant nonpregnant women for first time. Although CRC is present in both groups, reduced during pregnancy; there less synchronization maternal activity a smaller response heart rate to inhalations exhalations. Stratifying this analysis by sleep stages reveals that differences are most prominent deep sleep.

Язык: Английский

Процитировано

2
Studying sleep: towards the identification of hypnogram features that drive expert interpretation DOI
Caspar van der Woerd, Hans van Gorp,

Sylvie Dujardin

и другие.

SLEEP, Год журнала: 2023, Номер 47(3)

Опубликована: Дек. 1, 2023

Hypnograms contain a wealth of information and play an important role in sleep medicine. However, interpretation the hypnogram is difficult task requires domain knowledge "clinical intuition." This study aimed to uncover which features drive by physicians. In other words, make explicit physicians implicitly look for hypnograms. Three experts evaluated up 612 hypnograms, indicating normal or abnormal structure suspicion disorders. ElasticNet convolutional neural network classification models were trained predict collected expert evaluations using stages as input. The several measures, including accuracy, Cohen's kappa, Matthew's correlation coefficient, confusion matrices. Finally, model coefficients visual analytics techniques used interpret associate with evaluation. Agreement between (Kappa 0.47 0.52) similar agreement 0.38 0.50). Sleep fragmentation, measured transitions per hour, stage distribution identified predictors interpretation. By comparing hypnograms not solely on epoch-by-epoch basis, but also these more specific that are relevant evaluation experts, performance assessment (automatic) sleep-staging surrogate trackers may be improved. particular, fragmentation feature deserves attention it often included PSG report, existing (wearable) have shown relatively poor this aspect.

Язык: Английский

Процитировано

2
Temporal dynamics of awakenings from slow‐wave sleep in non‐rapid eye movement parasomnia DOI Creative Commons
Iris A. M. Huijben, Ruud J. G. van Sloun,

Bertram Hoondert

и другие.

Journal of Sleep Research, Год журнала: 2023, Номер 33(3)

Опубликована: Дек. 9, 2023

Summary Non‐rapid eye movement parasomnia disorders, also called disorders of arousal, are characterized by abnormal nocturnal behaviours, such as confusional arousals or sleep walking. Their pathophysiology is not yet fully understood, and objective diagnostic criteria lacking. It known, however, that behavioural episodes occur mostly in the beginning night, after an increase slow‐wave activity during sleep. A better understanding prospect may lead to new insights underlying mechanisms eventually facilitate diagnosis. We investigated temporal dynamics transitions from 52 patients 79 controls. Within patient group, non‐behavioural N3 awakenings were distinguished. Patients showed a higher probability wake up bout ended than controls, this increased with duration. Bouts longer 15 min resulted awakening 73% 34% time respectively. Behavioural reduced over cycles due reduction reducing ratio between awakenings. In first two cycles, bouts prior significantly shorter advancing patients, Our findings provide timing both N3, which result prediction potentially prevention episodes. This work, moreover, leads more complete characterization prototypical hypnogram parasomnias, could

Язык: Английский

Процитировано

2
Speckle Vibrometry for Contactless Instantaneous Heart Rate and Respiration Rate Monitoring on Mechanically Ventilated Patients DOI Creative Commons
Shuhao Que, Iris Cramer, Henk L. Dekker

и другие.

Sensors, Год журнала: 2024, Номер 24(19), С. 6374 - 6374

Опубликована: Окт. 1, 2024

: Contactless monitoring of instantaneous heart rate and respiration has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) contactlessly measure these two vital signs in an intensive care unit.

Язык: Английский

Процитировано

0
Respiration extraction and atrial fibrillation detection from clinical data based on single RGB camera DOI
Shuhao Que, Rik van Esch, Iris Cramer

и другие.

Опубликована: Апрель 10, 2023

In this work, we investigated the feasibility of extracting continuous respiratory parameters from a single RGB camera stationed in short-stay ward. Based on extracted respiration parameters, further using features to aid detection atrial fibrillation (AF). To extract respiration, implemented two algorithms: chest optical flow (COF) and energy variance maximization (EVM). We used COF patient's thoracic area EVM facial area. Using capnography as reference, for average breath-to-breath rate estimation (i.e., 15-second sliding windows with 50% overlap), achieved errors within 3 breaths per minute 3.5 EVM. detect presence AF signal, three derived measurements. fed these logistic regression model an AUC value 0.64. This result showcases potential camera-based predictors AF, or surrogate when there is no sufficient camera's field view extraction cardiac

Язык: Английский

Процитировано

1
Speckle Vibrometry for Instantaneous Heart Rate Monitoring DOI Creative Commons
Shuhao Que, Fokke van Meulen,

Willem Verkruijsse

и другие.

Sensors, Год журнала: 2023, Номер 23(14), С. 6312 - 6312

Опубликована: Июль 11, 2023

Instantaneous heart rate (IHR) has been investigated for sleep applications, such as apnea detection and staging. To ensure the comfort of patient during sleep, it is desirable IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) perform on-skin on-textile monitoring setting. Minute motions on laser-illuminated surface can captured by defocused camera, enabling cardiac even textiles. We investigate supine, lateral, prone sleeping positions. Based Bland–Altman analysis between SV measurements electrocardiogram (ECG), with respect each position, achieve best limits agreement ECG values [−8.65, 7.79] bpm, [−9.79, 9.25] [−10.81, 10.23] respectively. The results indicate potential using method instantaneous setting where participant allowed rest spontaneous position while covered textile layers.

Язык: Английский

Процитировано

1
Contactless monitoring for the elderly: potential and pitfalls DOI Creative Commons
Ju Lynn Ong, Kelly Glazer Baron

SLEEP, Год журнала: 2023, Номер 46(10)

Опубликована: Сен. 2, 2023

Язык: Английский

Процитировано

1
Smartphone-based and non-invasive sleep stage identification system with piezo-capacitive sensors DOI Creative Commons

Antonio J. Pérez-Ávila,

Noelia Ruiz‐Herrera,

Antonio Martínez-Olmos

и другие.

Sensors and Actuators A Physical, Год журнала: 2024, Номер 376, С. 115659 - 115659

Опубликована: Июнь 27, 2024

A non-invasive, wireless, smartphone-based electronic measurement system for sleep stage identification is presented in this work. Ballistocardiograph signals are collected by two piezo-capacitive thin film strips located on the mattress base. Suitable analog conditioning circuits and digital pre-processing techniques applied to obtain heart breathing rates (HR, BR), an activity index (ACT) related body movements during sleep. An initial calibration proposed where signal amplification fitted each subject, from which derived. Features considered machine learning classifications were mentioned data time variabilities of HR BR represented features R(k) B(k), respectively. Support Vector Machine (SVM) K-Nearest-Neighbour (KNN) classifiers employed both flat hierarchical classification scenarios Wake – Non Rapid Eye Movement (WAKE/NREM/REM) identification. Twelve healthy subjects recorded with developed using a polysomnograph (PSG) as reference data. When compared PSG, achieved average accuracy 69 % only three features: R(k), ACT, highlighting 88.2 recall NREM These findings suggest that accounting variability activity, satisfactory results can be provided complementary alternative identification, designed affordable, versatile, simple tool household applications.

Язык: Английский

Процитировано

0
Video-PSG: An Intelligent Contactless Monitoring System for Sleep Staging DOI
Qiongyan Wang, Hanrong Cheng, Wenjin Wang

и другие.

IEEE Transactions on Biomedical Engineering, Год журнала: 2024, Номер 72(3), С. 965 - 977

Опубликована: Окт. 15, 2024

Polysomnography (PSG) is the gold standard for sleep staging in clinics, but its skin-contact nature makes it uncomfortable and inconvenient to use long-term monitoring. As a complementary part of PSG, video cameras are not utilized their full potential, only manual check simple events, thereby ignoring potential physiological semantic measurement. This leads pivotal research question: Can camera be used staging, what extent? We developed camera-based contactless system Institute Respiratory Diseases created clinical dataset 20 adults. The feature set, derived from both signals (pulse breath) motions all measured video, was evaluated 4-class (Wake-REM-Light-Deep). Three optimization strategies were proposed enhance accuracy: using motion metrics prune measurement outliers, creating more personalized model based on baseline calibration waking-stage signals, deriving specialized REM detection. It achieved best accuracy 73.1% (kappa = 0.62, F1-score 0.74) benchmark five sleep-staging classifiers. Notably, exhibited high predicting overall structure subtle changes between different stages. study demonstrates that new value stream medicine, which also provides technical insights future implementation.

Язык: Английский

Процитировано

0