M-B. Naghibi-Sistani

Work place: Dept. of Electrical and Biomedical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran



Research Interests: Autonomic Computing, Computer Architecture and Organization, Computing Platform, Data Structures and Algorithms, Control Theory


Mohammad-Bagher Naghibi-Sistani He received the B.S. degree in electronics from the University of Tehran, Tehran, Iran, in 1991, the M.S. degree at control engineering from the University of Tehran, Tehran, Iran, in 1995, and the Ph.D. degree in control engineering from the Ferdowsi University of Mashhad, Iran, in 2005.

He currently is Assistant Professor at the Department of Electrical Engineering and Biomedical Engineering, Ferdowsi University of Mashhad.

His research interests include reinforcement learning, soft computing, optimal control, and Machine learning. He has published over 50 journal and conference papers.

Author Articles
Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition

By S. A. Hosseini M-R. Akbarzadeh-T M-B. Naghibi-Sistani

DOI: https://doi.org/10.5815/ijisa.2013.06.05, Pub. Date: 8 May 2013

A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the hurst exponent (H) and largest lyapunov exponent (λ). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and λ measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. Our inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 96.9% accuracy. Therefore, our method can be successfully applied to both inter-ictal and ictal data.

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