Title :
Automatic detection of non-convulsive seizures using AR modeling
Author :
Khan, Yusuf Uzzaman ; Farooq, Omar ; Tripathi, Meenakshi ; Sharma, Parmanand ; Alam, P.
Author_Institution :
Biosignal Res. Div., Aligarh Muslim Univ., Aligarh, India
Abstract :
This paper proposes an algorithm for the detection of non-convulsive seizures (NCSz), based on the Autoregressive (AR) modeling of EEG over the frequency band of 0-31 Hz. Simple linear classifier was used for the classification between the normal and seizure EEG. The algorithm was tested on the scalp EEG database of 5 patients, collected at All India Institute of Medical Sciences (AIIMS), New Delhi. The database consists of 13 seizures of different duration. The results were reported for 11 seizures. Overall sensitivity and specificity achieved by the method was 86.8% and 96.9% respectively.
Keywords :
autoregressive processes; electroencephalography; medical signal detection; signal classification; AIIMS; AR modeling; All India Institute of Medical Sciences; NCS; New Delhi; automatic detection; autoregressive modeling; frequency 0 Hz to 31 Hz; linear classifier; nonconvulsive seizures; normal EEG; scalp EEG database; seizure EEG; AR modeling; EEG; Linear classifier; Linear predictor coefficients (LPC); Non-convulsive seizures; Reflection coefficients;
Conference_Titel :
Power, Control and Embedded Systems (ICPCES), 2012 2nd International Conference on
Conference_Location :
Allahabad
Print_ISBN :
978-1-4673-1047-5
DOI :
10.1109/ICPCES.2012.6508099
