دانلود مقاله انگلیسی رایگان:بازنگری ارزش داده سندرم آپنه در بی خوابی: بیش از ملاقات چشم - 2020
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  • Revisiting the value of polysomnographic data in insomnia: more than meets the eye Revisiting the value of polysomnographic data in insomnia: more than meets the eye
    Revisiting the value of polysomnographic data in insomnia: more than meets the eye

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Revisiting the value of polysomnographic data in insomnia: more than meets the eye


    ترجمه فارسی عنوان مقاله:

    بازنگری ارزش داده سندرم آپنه در بی خوابی: بیش از ملاقات چشم


    منبع:

    Sciencedirect - Elsevier - Sleep Medicine, 66 (2020) 184-200: doi:10:1016/j:sleep:2019:12:002


    نویسنده:

    Thomas Andrillon a, b, *, Geoffroy Solelhac a, c, 1, Paul Bouchequet a, c, 1, Francesco Romano a, c, Max-Pol Le Brun d, Marco Brigham d, Mounir Chennaoui a, e, Damien Leger


    چکیده انگلیسی:

    Background: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). Method: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. Results: Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomnia patients and GS (Cohens k ¼ 0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features (k¼0.004). Conclusion: AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.
    Keywords: Artificial intelligence | Machine learning | Insomnia | Polysomnography | REM | NREM sleep


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 17
    حجم فایل: 1934 کیلوبایت

    قیمت: رایگان


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