Isfahan University of Medical Sciences Journal of Isfahan Medical School 1027-7595 31 243 2013 08 23 Seizure Diagnosis in Children based on the Electroencephalogram Modellind by Gaussian Process Model Seizure Diagnosis in Children based on the Electroencephalogram Modellind by Gaussian Process Model 985 996 14106 FA Zahra Amini PhD Student, Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran Hossein Rabbani Associate Professor, The Medical Image and Signal Processing Research Center AND Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran Journal Article 2013 02 13 Background: A seizure is the physical findings or changes in behavior occur after an episode of abnormal electrical activity in the brain. Seizures may interfere with cardiorespiratory function and with nutrition and may have detrimental long-term effects on cerebral development. Electroencephalogram (EEG) is essential in diagnosis and management of seizures. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the EEG.Methods: For automatic seizure detection, we used Gaussian process (GP) model and train it on the EEG signals recorded from some children between the ages of 1.5 to 16 years. After modeling EEG signal by GP model, two measures of output signal were derived: the variance of the predicted signal and the hyperparameter ratio. It was based on the hypotheses that because the EEG signal during seizure events is more deterministic and rhythmic, we can use the changing of these two criteria for seizure detection.Findings: During seizure events, the variance of the model output signal reduced and the hayperparameter ratio increased. The second measure was less successful but it had other advantages like robustness to model order selection.Conclusion: The GP modeling is a good method for seizure detection. Important objectives are to perform this detection as quickly, efficiently and accurately as possible. In this method, decisions are made accurate and with negligible delay. Background: A seizure is the physical findings or changes in behavior occur after an episode of abnormal electrical activity in the brain. Seizures may interfere with cardiorespiratory function and with nutrition and may have detrimental long-term effects on cerebral development. Electroencephalogram (EEG) is essential in diagnosis and management of seizures. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the EEG.Methods: For automatic seizure detection, we used Gaussian process (GP) model and train it on the EEG signals recorded from some children between the ages of 1.5 to 16 years. After modeling EEG signal by GP model, two measures of output signal were derived: the variance of the predicted signal and the hyperparameter ratio. It was based on the hypotheses that because the EEG signal during seizure events is more deterministic and rhythmic, we can use the changing of these two criteria for seizure detection.Findings: During seizure events, the variance of the model output signal reduced and the hayperparameter ratio increased. The second measure was less successful but it had other advantages like robustness to model order selection.Conclusion: The GP modeling is a good method for seizure detection. Important objectives are to perform this detection as quickly, efficiently and accurately as possible. In this method, decisions are made accurate and with negligible delay. https://jims.mui.ac.ir/article_14106_8b2af218c67338fe19c095227bd73969.pdf