title: Backpropagation artificial neural network detects changes in electro-encephalogram power spectra of syncopic patients.
abstract: This paper presents an effective application of backpropagation artificial neural network (ANN) in differentiating electroencephalogram (EEG) power spectra of syncopic and normal subjects. Digitized 8-channel EEG data were recorded with standard electrodes placement and amplifier settings from five confirmed syncopic and five normal subjects. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation and analysis of changes due to syncope. The results revealed significant increase in percentage delta and alpha (p < 0.5 or better) with significant reduction in percentage theta activity (p < 0.05). The backpropagation ANN used for classification contains 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from syncopic EEG power spectra and the normal EEG power spectra with an accuracy of 88.87% (85.75% for syncopic and 92% for normal).
authors.affiliation: Department of Biomedical Instrumentation, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835215, India. rksinha_res@rediffmail.com
authors.forename: Rakesh Kumar
authors.initials: RK
authors.lastname: Sinha
authors.forename: Yogender
authors.initials: Y
authors.lastname: Aggarwal
authors.forename: Barda Nand
authors.initials: BN
authors.lastname: Das
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D016022:Case-Control Studies; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D005246:Feedback; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013575:Syncope / Q000175:diagnosis* / Q000473:pathology; D013687:Telencephalon / Q000473:pathology