Browsing by Subject "Neurophysiology"
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Item Open Access All-fiber nanosecond laser system generating supercontinuum spectrum for photoacoustic imaging(IEEE, 2013) Yavas, S.; Kipergil, E. A.; Akçaalan, Önder; Eldeniz, Y. Burak; Arabul, U.; Erkol H.; Unlu, M.B.; Ilday, F. ÖmerPhotoacoustic microscopy (PAM) research, as an imaging modality, has shown promising results in imaging angiogenesis and cutaneous malignancies like melanoma, revealing systemic diseases including diabetes, hypertension, coronery artery, cardiovascular disease from their effect on the microvasculature, tracing drug efficiency and assessment of therapy, monitoring healing processes such as wound cicatrization, brain imaging and mapping, neuroscientific evaluations. Clinically, PAM can be used as a diagnostic and predictive medicine tool; even have a part in disease prevention[1]. © 2013 IEEE.Item Open Access Seizure detection using least eeg channels by deep convolutional neural network(Institute of Electrical and Electronics Engineers Inc., 2019) Avcu, Mustafa Talha; Zhang, Z.; Chan, D. W. S.This work aims to develop an end-to-end solution for seizure onset detection. We design the SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with traditional machine learning approach, a baseline classifier is implemented using spectrum band power features with Support Vector Machines (BPsvm). We explore the possibility to use the least number of channels for accurate seizure detection by evaluating SeizNet and BPsvm approaches using all channels and two channels settings respectively. EEG Data is acquired from 29 pediatric patients admitted to KK Woman's and Children's Hospital who were diagnosed as typical absence seizures. We conduct leave-one-out cross validation for all subjects. Using full channel data, BPsvm yields a sensitivity of 86.6% and 0.84 false alarm (per hour) while SeizNet yields overall sensitivity of 95.8 % with 0.17 false alarm. More interestingly, two channels seizNet outperforms full channel BPsvm with a sensitivity of 93.3% and 0.58 false alarm. We further investigate interpretability of SeizNet by decoding the filters learned along convolutional layers. Seizure-like characteristics can be clearly observed in the filters from third and forth convolutional layers.