Seizure onset detection based on detection of changes in brain activity quantified by evolutionary game theory model. (February 2021)

• Record Type: Journal Article
• Authors: Hamavar, Ramtin; Asl, Babak Mohammadzadeh
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: Seizure onset detection -- Evolutionary game theory -- Kalman filter -- Brain activity modelling -- Epilepsy -- Electroencephalogram; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105899
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel framework for modeling brain activity is proposed. Better results compared to recent studies are achieved with much fewer execution time. The change in brain activity during a seizure is quantifi ed by using evolutionary game theory. Automatic update of estimation parameters is proposed to handle change in patients brain activity. Seizure onsets are detected at 100% sensitivity and 0.39/h average false alarm. Abstract: Background and Objective : Epilepsy is one of the most common diseases of the nervous system, affecting about 1% of the world's population. The unpredictable nature of the epilepsy seizures deprives the patients and those around them of living a normal life. Therefore, the development of new methods that can help these patients will increase the life quality of these people and can bring a lot of economic savings in the health sector. Methods : In this study, we introduced a new framework for seizure onset detection. Our framework provides a new modelling for brain activity using evolutionary game theory and Kalman filter. If the patterns in the electroencephalogram (EEG) signal violate the predicted patterns by the proposed model, using a novel detection algorithm that has been also introduced in this paper, it can be determined whether the observed violation is the result of the onset of an epileptic seizure or not. Results : The proposed approach was able to detect the onset of all the seizures in CHB-MIT dataset with an average delay of − 0.8 s and a false alarm of 0.39 per hour. Also, our proposed approach is about 20 times faster compared to recent studies. Conclusions : The experimental results of applying the proposed framework on the CHB-MIT dataset show that our framework not only performed well with respect to the sensitivity, delay, and false alarm metrics but also performed much better in terms of run time compared to recent studies. This appropriate run time, along with other suitable metrics, makes it possible to use this framework in many cases where processing power or energy is limited and to think about creating new and inexpensive solutions for the treatment and care of people diagnosed with epilepsy.