Utility of the psychomotor vigilance task in screening for obstructive sleep apnoea

Ma, Jingru; Qiu, Xihe; Sun, Lijie; Cong, Ning; Wei, Yingchen; Wei, Chunsheng; Huang, Jingjing

doi:10.1007/s00405-023-08373-3

Eur Arch Otorhinolaryngol

2024

DOI: 10.1007/s00405-023-08373-3

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Utility of the psychomotor vigilance task in screening for obstructive sleep apnoea

Jingru Ma,

Xihe Qiu,

Lijie Sun

et al.

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2024

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Cited by 2 publications

References 27 publications

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An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds

Qiu,

Wang,

et al. 2024

Front. Neurosci.

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IntroductionObstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep-related breathing disorder that significantly impacts the daily lives of patients. Currently, the diagnosis of OSAHS relies on various physiological signal monitoring devices, requiring a comprehensive Polysomnography (PSG). However, this invasive diagnostic method faces challenges such as data fluctuation and high costs. To address these challenges, we propose a novel data-driven Audio-Semantic Multi-Modal model for OSAHS severity classification (i.e., ASMM-OSA) based on patient snoring sound characteristics.MethodsIn light of the correlation between the acoustic attributes of a patient's snoring patterns and their episodes of breathing disorders, we utilize the patient's sleep audio recordings as an initial screening modality. We analyze the audio features of snoring sounds during the night for subjects suspected of having OSAHS. Audio features were augmented via PubMedBERT to enrich their diversity and detail and subsequently classified for OSAHS severity using XGBoost based on the number of sleep apnea events.ResultsExperimental results using the OSAHS dataset from a collaborative university hospital demonstrate that our ASMM-OSA audio-semantic multimodal model achieves a diagnostic level in automatically identifying sleep apnea events and classifying the four-class severity (normal, mild, moderate, and severe) of OSAHS.DiscussionOur proposed model promises new perspectives for non-invasive OSAHS diagnosis, potentially reducing costs and enhancing patient quality of life.

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An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds

Qiu,

Wang,

et al. 2024

Front. Neurosci.

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Sleep Disordered Breathing and Neurocognitive Disorders

Ogbu,

Menon,

Chahil

et al. 2024

JCM

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Sleep-disordered breathing (SDB), which includes conditions such as obstructive sleep apnea (OSA) and central sleep apnea (CSA), is an independent risk factor for cerebral small vessel disease (CSVD), stroke, heart failure, arrhythmias, and other cardiovascular disorders. The influence of OSA on brain structure and cognitive function has become an essential focus in the heart-brain axis, given its potential role in developing neurocognitive abnormalities. In this review, we found that OSA plays a significant role in the cardio-neural pathway that leads to the development of cerebral small vessel disease and neurocognitive decline. Although data is still limited on this topic, understanding the critical role of OSA in the heart-brain axis could lead to the utilization of imaging modalities to simultaneously identify early signs of pathology in both organ systems based on the known OSA-driven pathological pathways that result in a disease state in both the cardiovascular and cerebrovascular systems. This narrative review aims to summarize the current link between OSA and neurocognitive disorders, cardio-neural pathophysiology, and the treatment options available for patients with OSA-related neurocognitive disorders.

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Utility of the psychomotor vigilance task in screening for obstructive sleep apnoea

Cited by 2 publications

References 27 publications

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds

Sleep Disordered Breathing and Neurocognitive Disorders

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