Genes Associated with Altered Brain Structure and Function in Obstructive Sleep Apnea

Huang, Yijie; Shen, Chong; Zhao, Wei; Shang, Youlan; Wang, Yisong; Zhang, Hui-Ting; Ouyang, Ruoyun; Liu, Jun

doi:10.3390/biomedicines12010015

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…Huang et al 13 used voxel-based morphology and the amplitude of low-frequency fluctuations analyses to compare cortical morphology and spontaneous brain activity obstructive sleep apnea (OSA) and healthy subjects, including 28 moderate-to-severe OSA and 34 healthy subjects, respectively. The Allen Human Brain Atlas and transcriptome-neuroimaging spatial correlation analysis were used for the investigation of gene expression patterns associated with changes in grey matter volume and amplitude of low-frequency fluctuations in OSA.…”

Section: Introductionmentioning

confidence: 99%

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Rajeswari,

Jagannath

2024

Sci Rep

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Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics—University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics—University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.

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Section: Introductionmentioning

confidence: 99%