caa a22 4500 606206221 CHVBK 20210128101010.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00034-015-9982-y doi (NATIONALLICENCE)springer-10.1007/s00034-015-9982-y EEG Spike Detection Technique Using Output Correlation Method: A Kalman Filtering Approach [Elektronische Daten] [Harish Garg, Amit Kohli] This correspondence presents a technique for the electroencephalogram (EEG) spike enhancement and detection, which uses the Kalman filtering (KF) approach based on the output correlation method for the nonstationary signal enhancement. We describe the nonstationary EEG signal in terms of the general Markov model, in which the parameters are considered to be time-varying. In the proposed methodology, neither the process and measurement noise statistics nor the initial Kalman blending factor are stringently required. The EEG epileptic spikes (ESs) are pre-emphasized using the output correlation method, and subsequently, the detection is performed using the decision threshold based on the output of same adaptive filter. We have tested the proposed scheme on the synthetic EEG signal corrupted with randomly occurring triangular spikes. The presented simulation results manifest significant improvement in the signal-to-noise ratio (SNR) due to the modified estimation of time-varying parameters of the general Markov model, which in turn leads to the alleviated number of false-positives (FPs). It is apparent that the real-time EEG signal (rat data) can be analyzed using the proposed EEG epileptic spike enhancement and detection adaptive scheme, which outperforms the conventional KF technique under the different SNR conditions. At 10dB SNR, the output correlation method provides approximately 40% reduction in FPs for the triangular spikes in synthetic EEG signal and approximately 27.5% reduction in FPs for ESs in the rat data as compared to the conventional KF scheme. Springer Science+Business Media New York, 2015 Electroencephalogram (EEG) nationallicence Epileptic spikes (ESs) nationallicence Kalman filter (KF) nationallicence Correlation nationallicence Time-varying environments/channels nationallicence Adaptive signal processing nationallicence Markov model nationallicence Garg Harish Department of Electronics and Communication Engineering, Thapar University, 147004, Patiala, Punjab, India aut Kohli Amit Department of Electronics and Communication Engineering, Thapar University, 147004, Patiala, Punjab, India aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/8(2015-08-01), 2643-2665 0278-081X 34:8<2643 2015 34 34 https://doi.org/10.1007/s00034-015-9982-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-015-9982-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Garg Harish Department of Electronics and Communication Engineering, Thapar University, 147004, Patiala, Punjab, India aut NATIONALLICENCE 700 1- Kohli Amit Department of Electronics and Communication Engineering, Thapar University, 147004, Patiala, Punjab, India aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/8(2015-08-01), 2643-2665 0278-081X 34:8<2643 2015 34 34