Interhemispheric Brain Switching Correlates with Severity of Sleep-Disordered Breathing for Obstructive Sleep Apnea Patients

Li, Yuwen; Zhang, Zhimin; Zhu, Guohun; Gan, Hongping; Liu, Deyin; Weng, Wei Zheng; Wei, Shoushui

doi:10.3390/app9081568

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…According to (10)(11)(12)(13), ELI is computed as the sum of λ times of the LI calculated based on the signal complexity and 1 − λ times of the LI calculated based on the signal strength.…”

Section: Enhanced Laterality Indexmentioning

confidence: 99%

“…It is widely accepted that both cerebral hemispheres are in constant communications with each other during all kinds of brain activities [8]. At the same time, the di erences between left and right cerebral hemispheres have been reported in existing studies with EEG [9, 10] and fMRI [11], including language, vision, audition, and memory [12].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Laterality Index: A Novel Measure for Hemispheric Asymmetry

Zhang²

2022

Journal of Healthcare Engineering

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During sleep, the two hemispheres display asymmetries in their activation pattern. Various hemispheric asymmetry measures have been utilized in existing works. Nevertheless, all these measures have one common problem that they would merely take one representative quantity into account when evaluating the functional asymmetry. However, there is a complex series of information exchanges between the two cerebral hemispheres, and only considering one quantity inevitably leads to one-sided or even incorrect conclusions. Consequently, to address the limitation of conventional laterality indices, we propose the so-called enhanced laterality index (ELI), which considers multiple measures of functional asymmetries. Normal sleep and obstructive sleep apnea electroencephalograms (EEGs) from 21 subjects collected in the clinical acquisition system are applied, and two representative quantities are considered simultaneously in this paper. We measure the signal complexity by using fuzzy entropy, and the signal strength is evaluated by calculating EEG energy. The difference of ELI is demonstrated by the comparison with the traditional laterality index (LI) in evaluating the functional asymmetry during sleep.

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Section: Enhanced Laterality Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Laterality Index: A Novel Measure for Hemispheric Asymmetry

Zhang²

2022

Journal of Healthcare Engineering

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“…Bruxism is an under-recognized cause of caregiver concern in patients with Alzheimer's disease [17]. The interhemispheric brain switching of sleep disordered breathing problem in obstructive sleep apnea patients were correlated by Li et al [18]. The fuzzy entropy was used to estimate a laterality index from C3-A2 and C4-A1 EEG channels.…”

Section: Introductionmentioning

confidence: 99%

Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG

et al. 2019

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Lack of sleep causes many sleep disorders such as nocturnal frontal lobe epilepsy, narcolepsy, bruxism, sleep apnea, insomnia, periodic limb movement disorder, and rapid eye movement behavioral disorder. Out of all, bruxism is a common behavior, which is found in 8-31% of the population. Bruxism is a sleep disorder in which individuals involuntarily grinds and clenches the teeth. The main aim of this work is to detect sleep bruxism by analyzing the electroencephalogram (EEG) spectrum analysis of the change in the domain of different stages of sleep. The present research was performed in different stages such as collection of the data, preprocessing of the EEG signal, analysis of the C4-P4 and C4-A1 channels, comparison between healthy humans and bruxism patients, and classification using decision tree method. In this study, the channels C4-P4 and C4-A1 of the EEG signal were combined for the detection of bruxism by using Welch technique, which mainly focused on two sleep stages such as S1 and rapid eye movement. The total number of EEG channels of healthy humans and bruxism patients analyzed in this work were 15 and 18, respectively. The results showed that the individual accuracy of the C4-P4 and C4-A1 channels was 81.70% and 74.11%, respectively. The combined accuracy of both C4-P4 and C4-A1 channels was 81.25%. The specificity of combined result was higher than individual. In addition, the value of theta activity during detection is consistent throughout the period, and the accuracy of S1 stage is better than rapid eye movement stage. We proposed that the theta activity of S1 could be taken for the detection of bruxism. The proposed approach in the detection of the bruxism is negligible in noise as it is in mathematical form and has taken very less time as compared with the traditional systems. The present research work would provide a fast and effective detection system of the sleep bruxism with high accuracy for medical big data applications. INDEX TERMS Decision tree, machine learning classifier, neurological disorder, scalp EEG, sleep bruxism.

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