EEG SIGNALS PROCESSING FOR DIAGNOSIS PETITMAL (ABSENCE) AND GRANDMAL EPILEPSIES USING ARTIFICIAL NEURAL NETWORK

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ARAB M.R.; SOURATGAR A.A.A.F.; REZAEI ASHTIANI A.R.

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Journal: Journal of Arak University Medical Sciences Year:2008 | Volume:11 | Issue:3 (44) Page(s): 89-97

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Title

EEG SIGNALS PROCESSING FOR DIAGNOSIS PETITMAL (ABSENCE) AND GRANDMAL EPILEPSIES USING ARTIFICIAL NEURAL NETWORK

Author(s)

ARAB M.R. | SOURATGAR A.A.A.F. | REZAEI ASHTIANI A.R. | Issue Writer Certificate

Keywords

ELECTROENCEPHALOGRAMQ3

GRANDMAL EPILEPSYQ3

PETITMAL EPILEPSYQ3

ABSENCE EPILEPSYQ3

Abstract

Background: Epileptic seizures are manifestation of epilepsy. Understanding of the mechanisms causing epileptic disorder needs careful analyses of the electroencephalograph (EEG) records. The detection of epileptic form discharges (spike wave) in the EEG is an important component in the diagnosis of epilepsy. Approximately one in every 100 persons will experience a seizure at some time in their life. Already intelligence spike detection method discussed but purpose of this research is diagnosis of different kind of epilepsy (grandmal and Petitmal) by design of an intelligence diagnosis processing.Methods and Materials: In this descriptive study, 100 EEG signals of brain hemispheres from different person in healthy, interictal and ictal conditions were used. Fifty Hz noise and artifact signals were removed by soft ware procedure then signals separated by expert neurologist to three categories, healthy (frequency band 8-12 Hz), petitmal seizures (typical 3 Hz), grandmal seizures (clonic stage with 4 Hz frequency) and divided each of them to 6 seconds segments. Information of this signals (background alpha, spike and slow, poly spike and poly sharp) were extracted by wavelet transform and classified by soft ware procedure neural network to their groups healthy, ptitmal and GRANDMAL EPILEPSY. Results: In designed software accuracy of diagnosis ptitmal and grandmal epilepsies was obtained about 80%.Conclusion: This method introduced intelligent diagnosis of epilepsy (ptitmal and gradmal) and automatically detected healthy person from epileptic patients. One of the other advantages is help to neurologist for detection of sickness clearly and expendable different kinds of other epilepsy.

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APA: Copy

ARAB, M.R., SOURATGAR, A.A.A.F., & REZAEI ASHTIANI, A.R.. (2008). EEG SIGNALS PROCESSING FOR DIAGNOSIS PETITMAL (ABSENCE) AND GRANDMAL EPILEPSIES USING ARTIFICIAL NEURAL NETWORK. ARAK MEDICAL UNIVERSITY JOURNAL (AMUJ), 11(3 (44)), 89-97. SID. https://sid.ir/paper/69464/en

Vancouver: Copy

ARAB M.R., SOURATGAR A.A.A.F., REZAEI ASHTIANI A.R.. EEG SIGNALS PROCESSING FOR DIAGNOSIS PETITMAL (ABSENCE) AND GRANDMAL EPILEPSIES USING ARTIFICIAL NEURAL NETWORK. ARAK MEDICAL UNIVERSITY JOURNAL (AMUJ)[Internet]. 2008;11(3 (44)):89-97. Available from: https://sid.ir/paper/69464/en

IEEE: Copy

M.R. ARAB, A.A.A.F. SOURATGAR, and A.R. REZAEI ASHTIANI, “EEG SIGNALS PROCESSING FOR DIAGNOSIS PETITMAL (ABSENCE) AND GRANDMAL EPILEPSIES USING ARTIFICIAL NEURAL NETWORK,” ARAK MEDICAL UNIVERSITY JOURNAL (AMUJ), vol. 11, no. 3 (44), pp. 89–97, 2008, [Online]. Available: https://sid.ir/paper/69464/en

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