
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 2, 2024
Язык: Английский
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 2, 2024
Язык: Английский
Journal of Applied Biomedicine, Год журнала: 2024, Номер 44(3), С. 651 - 673
Опубликована: Июнь 29, 2024
Язык: Английский
Процитировано
10Physiological 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
3SLEEP, 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
3Journal 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
0Journal 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
0Journal of Medical Systems, Год журнала: 2025, Номер 49(1)
Опубликована: Май 19, 2025
Язык: Английский
Процитировано
0Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 2, 2024
Язык: Английский
Процитировано
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