Separation Of Delta , Theta , Alpha & Beta Activities In EEG To Measure The Depth Of Sleep And Mental Status
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|Year of Publication : 2013|
|Authors : Shah Aqueel Ahmed , Syed Abdul Sattar , D. Elizabath Rani|
Shah Aqueel Ahmed , Syed Abdul Sattar , D. Elizabath Rani. "Separation Of Delta , Theta , Alpha & Beta Activities In EEG To Measure The Depth Of Sleep And Mental Status ". International Journal of Engineering Trends and Technology (IJETT). V4(10):4618-4623 Oct 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.
The electrical activity of the human brain i.e. the EEG and its classification into various frequency bands has been of interest to researches dealing with neurology. So, here our aim is targeted to classify EEG signals traces into different fundamental frequency rhythms and determine whether a relatively short EEG record taken in a routine laboratory is normal or abnormal. The primary aim of computerized EEG analysis is to support electroencephalographer’s evaluation, by representing the data in numerical and or graphical form. EEG analysis however, can go further, actually extending the electroencephalographer’s capabilities giving them new tools with which they can perform such difficult and time consuming tasks as quantitative duration EEG in epileptic patients and sleep and psychopharmacological studies. This method is having several advantages over the visual screening by which it is very difficult to extract EEG information. The choice of analytic method should be determined mainly by the goal of the application. The frequency domain tool is used for EEG analysis. The system performs continuous analysis in graphical form and tabular form of recorded EEG signal. Algorithm is implemented by using C language. It is helpful to classify the depth of sleep and mental status from the percentage power in each band i.e., delta, alpha, beta and theta.
 Fernando lopes da Silva, “EEG analysis: theory and practice”
 R S Khandpur, “biomedical instrumentation and measurement”
 Haroid J Kaplan, “comprehensive textbook of psychiatry- III”
 John r Glover, Periklis Y Kotonas and Narasimhan Raghaavan “a multichannel signal processor for the detection of epileptogenic sharp transients in the EEG”, IEEE transactions on biomedical engineering vol. BME, 33 no. 12, December 1986
 Alison A dingle, Richard D Jones and others “A multistage system to detect epileptiform activity in the EEG”, IEEE transaction on biomedical engineering, vol. 40, no., December 1993
 Johnny R Johnson “introduction to digital signal processing”
 John G Proakis “ digital signal processing”
 “The time domain analysis tool for EEG analysis” by S.Park, J. Smith, IEEE transaction on BME, Vol. 37,august 1997
 “Estimating alertness from the EEG power spectrum” by Magnus Stuismo, T.J. Signowski, IEEE transaction on BME, Vol. 44, No. 1 Jan. 1997
 Seughun park and Jose C principle, “time domain analysis tool for EEG analysis”, IEEE transactions on biomedical engineering, vol. 22, no.8, august 1984
 “Introduction to Biomedical Equipment Technology” by Joseph J. Carr & John M. Brown.
Power Spectrum, EEG, Delta, Theta, Alpha, Beta