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양극성장애 환자와 대조군에서 뇌파 코히런스의 성별 차이
유현주,이유상,안은숙,정동화,김성균,정재승,곽용태,이승연,You, Hyunju,Lee, Yu Sang,An, Eunsoog,Jeong, Donghwa,Kim, Seongkyun,Jeong, Jaeseung,Kwak, Yongtae,Lee, Seungyeoun 대한생물정신의학회 2015 생물정신의학 Vol.22 No.4
Objectives Sex hormones exposure during the prenatal period has an effect on cerebral lateralization. Male brains are thought to be more lateralized than female brains. Bipolar disorder was known to show abnormalities in cerebral laterality whose characteristics could be estimated by electroencephalography (EEG) coherences. We studied sex-related differences of EEG coherences between healthy controls and patients with bipolar disorder to examine the sex effects in the genesis of bipolar disorder. Methods Participants were 25 patients with bipolar disorder (11 male, 14 female) and 46 healthy controls (23 male, 23 female). EEG was recorded in the eyes closed resting state. To examine dominant EEG coherence associated with sex differences in both groups within five frequency bands (delta, theta, alpha, beta, and gamma) across several brain regions, statistical analyses were performed using analysis of covariance. Results Though statistically meaningful results were not found, some remarkable findings were noted. Healthy control females showed more increased interhemispheric coherences than control males in gamma frequency band. There were no differences in the intrahemispheric coherences between the healthy control males and females. In patients with bipolar disorder, female dominant pattern in interhemispheric coherences was attenuated compared with healthy control. Conclusions Sex differences of EEG coherences, which could be a marker for cerebral laterality, were attenuated in patients with bipolar disorder compared with healthy controls. These results imply that abnormal sex hormone exposure during early development might play some role in the pathogenesis of bipolar disorder.
강소희(Sohee Kang),김용겸(Younggyeom Kim),김유신(Yousin Kim),배승용(Seungyong Bae),박지현(Jihyun Park),이정환(Jeonghwan Lee),정동화(Donghwa Jeong),임완수(Wansu Lim) 대한전자공학회 2018 대한전자공학회 학술대회 Vol.2018 No.11
In this paper, we introduce sign recognition algorithms to help facilitate communication between hearing-impaired using sign language and speech-language users. The algorithm is developed to use Microsoft"s depth sensor, kinect, to receive human joint information and to analyze sign language by learning joint information in neural networks. This neural networks is formed models that analyze arm movement and finger, about 88% accuracy was achieved in the test set.