Wearable Sensors and Artificial Intelligence for sleep apnea detection: A Systematic Review DOI Creative Commons
Ainhoa Osa-Sanchez,

Javier Ramos-Martinez-de-Soria,

Amaia Méndez Zorrilla

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 2, 2024

Abstract Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality life. The integration wearable devices artificial intelligence technologies revolutionized the treatment diagnosis sleep apnea. Leveraging portability sensors devices, coupled with AI algorithms, enabled real-time monitoring accurate analysis patterns, facilitating early detection personalized interventions for suffering from This review articles presents systematic current state art identifying latest techniques, data types, preprocessing methods employed Four databases were used results before screening report 249 studies published between 2020 2024. After screening, 28 met inclusion criteria. reveals trend where methodologies involving patches, clocks rings have been increasingly integrated convolutional neural networks, producing promising results, particularly when combined transfer learning techniques. We observed that outcomes various algorithms their combinations also rely quantity type utilized training. findings suggest employing multiple different networks layers contributes development more precise system

Язык: Английский

A review of automated sleep stage based on EEG signals DOI

Xiaoli Zhang,

Xizhen Zhang, Qiong Huang

и другие.

Journal of Applied Biomedicine, Год журнала: 2024, Номер 44(3), С. 651 - 673

Опубликована: Июнь 29, 2024

Язык: Английский

Процитировано

10

Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders DOI Creative Commons
Hans van Gorp, Merel M. van Gilst, Sebastiaan Overeem

и другие.

Physiological Measurement, Год журнала: 2024, Номер 45(5), С. 055007 - 055007

Опубликована: Апрель 23, 2024

Sleep staging based on full polysomnography is the gold standard in diagnosis of many sleep disorders. It however costly, complex, and obtrusive due to use multiple electrodes. Automatic single-channel electro-oculography (EOG) a promising alternative, requiring fewer electrodes which could be self-applied below hairline. EOG algorithms are yet validated clinical populations with

Язык: Английский

Процитировано

3

Refining sleep staging accuracy: transfer learning coupled with scorability models DOI
Wolfgang Ganglberger, Samaneh Nasiri, Haoqi Sun

и другие.

SLEEP, Год журнала: 2024, Номер 47(11)

Опубликована: Авг. 31, 2024

Abstract Study Objectives This study aimed to (1) improve sleep staging accuracy through transfer learning (TL), achieve or exceed human inter-expert agreement and (2) introduce a scorability model assess the quality trustworthiness of automated staging. Methods A deep neural network (base model) was trained on large multi-site polysomnography (PSG) dataset from United States. TL used calibrate reduced montage limited samples Korean Genome Epidemiology (KoGES) dataset. Model performance compared reliability among three experts. assessment developed predict between Results Initial by base showed lower with experts (κ = 0.55) 0.62). Calibration 324 randomly sampled training cases matched expert levels. Further targeted sampling improved performance, models exceeding 0.70). The assessment, combining biosignal confidence features, predicted model-expert moderately well (R² 0.42). Recordings higher scores demonstrated greater than agreement. Even scores, comparable Conclusions Fine-tuning pretrained significantly enhances for an atypical montage, achieving surpassing introduction provides robust measure reliability, ensuring control enhancing practical application system before deployment. approach marks important advancement in analysis, demonstrating potential AI clinical settings.

Язык: Английский

Процитировано

3

Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV‐Based Sleep Staging Models DOI Creative Commons
Jaap F. van der Aar, Merel M. van Gilst, Daan van den Ende

и другие.

Journal of Sleep Research, Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

ABSTRACT Rapid‐eye‐movement (REM) sleep behaviour disorder (RBD) is a primary strongly associated with Parkinson's disease. Assessing structure in RBD important for understanding the underlying pathophysiology and developing diagnostic methods. However, performance of automated stage classification (ASSC) models considered suboptimal RBD, both utilising neurological signals (“ExG”: EEG, EOG, chin EMG) heart rate variability combined body movements (HRVm). Here, we explore this underperformance through categorical representation macrostructure (i.e., hypnogram) that leverages probability distribution ASSCs hypnodensity). By comparing population ( n = 36) to sex‐ age‐matched group OSA patients chosen their anticipated similarly decreased stability, confirm lower 4‐stage ExG‐based ASSC (RBD: κ 0.74, OSA: 0.80) HRVm‐based 0.50, 0.63). Stages showing agreement namely, N1 + N2 REM sleep, exhibited elevated ambiguity hypnodensity, indicating more ambiguous distributions. Limited differences bout durations between suggested instability not necessarily driving RBD. transitions showed abrupt changes distribution, while had continuous profile, possibly complicating classification. Although staging remain challenging, hypnodensity analysis informative characterisation can capture potential drivers disagreement.

Язык: Английский

Процитировано

0

About Digitalisation and AI, Data Protection, Data Exchange, Data Mining—Legal Constraints/Challenges Concerning Sleep Medicine DOI Creative Commons
Bernd Feige, Fee Benz, Raphael J. Dressle

и другие.

Journal of Sleep Research, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

ABSTRACT The revolution of artificial intelligence (AI) methods in the scope last years has inspired a deluge use cases but also caused uncertainty about actual utility and boundaries these methods. In this overview, we briefly introduce their main characteristics before focusing on sleep medicine, discriminating four areas: Measuring state, advancing diagnostics, research general advances. We then outline current European legal framework AI related topic data sharing.

Язык: Английский

Процитировано

0

Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review DOI Creative Commons
Ainhoa Osa-Sanchez,

Javier Ramos-Martinez-de-Soria,

Amaia Méndez Zorrilla

и другие.

Journal of Medical Systems, Год журнала: 2025, Номер 49(1)

Опубликована: Май 19, 2025

Язык: Английский

Процитировано

0

Wearable Sensors and Artificial Intelligence for sleep apnea detection: A Systematic Review DOI Creative Commons
Ainhoa Osa-Sanchez,

Javier Ramos-Martinez-de-Soria,

Amaia Méndez Zorrilla

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 2, 2024

Abstract Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality life. The integration wearable devices artificial intelligence technologies revolutionized the treatment diagnosis sleep apnea. Leveraging portability sensors devices, coupled with AI algorithms, enabled real-time monitoring accurate analysis patterns, facilitating early detection personalized interventions for suffering from This review articles presents systematic current state art identifying latest techniques, data types, preprocessing methods employed Four databases were used results before screening report 249 studies published between 2020 2024. After screening, 28 met inclusion criteria. reveals trend where methodologies involving patches, clocks rings have been increasingly integrated convolutional neural networks, producing promising results, particularly when combined transfer learning techniques. We observed that outcomes various algorithms their combinations also rely quantity type utilized training. findings suggest employing multiple different networks layers contributes development more precise system

Язык: Английский

Процитировано

0