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