Title :
Detection of characteristic wave in EEG using locally stationary AR model
Author :
Fukami, Tadanori ; Akatsuka, Takao ; Saito, Yoichi
Author_Institution :
Fac. of Eng., Yamagata Univ., Japan
Abstract :
It is much important to detect the characteristic wave in EEG clinical diagnosis. They are classified into three groups. One is a stationary wave such as an alpha or beta wave, the others are burst wave (semi-transient wave) and transient wave, nonstationary wave, such as hump wave. The final goal of our research is labeling of EEG wave in short section. In this research, we tried to detect a hump wave by using a locally stationary AR model as a first trial. We employed this method for clinical EEG data. The accuracy of detection showed a 76% level.
Keywords :
electroencephalography; medical signal detection; medical signal processing; physiological models; EEG characteristic wave detection; alpha wave; autoregressive model; beta wave; burst wave; clinical EEG data; detection accuracy; electrodiagnostics; hump wave detection; locally stationary AR model; nonstationary wave; transient wave; Brain modeling; Electroencephalography; Equations; Filtering; Fluctuations; Frequency; Heart rate; Kalman filters; Shape; Time series analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1020585
