Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network

• Published in: IEEE transactions on biomedical engineering Vol. 53; no. 4; pp. 633 - 641
• Main Authors: Karayiannis, N.B., Mukherjee, A., Glover, J.R., Ktonas, P.Y., Frost, J.D., Hrachovy, R.A., Mizrahi, E.M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological neural networks; Brain - physiopathology; Detectors; Diagnosis, Computer-Assisted - methods; Electroencephalography; Electroencephalography - methods; Epilepsy; Epilepsy, Benign Neonatal - diagnosis; Epilepsy, Benign Neonatal - physiopathology; epileptic seizure segment; feedforward neural network (FFNN); Feedforward neural networks; Frequency; Humans; Infant, Newborn; Intelligent networks; Morphology; neonatal seizure; Neural networks; Neural Networks (Computer); Pattern Recognition, Automated - methods; Pediatrics; quantum neural network (QNN); Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2006.870249

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.