Circuits, Systems, and Signal Processing

, Volume 34, Issue 8, pp 2643–2665 | Cite as

EEG Spike Detection Technique Using Output Correlation Method: A Kalman Filtering Approach



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 10 dB 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.


Electroencephalogram (EEG) Epileptic spikes (ESs) Kalman filter (KF) Correlation Time-varying environments/channels  Adaptive signal processing Markov model 



Authors are thankful to Dr. Poonam Gakhar Kohli, Associate Professor, Physiology Department, Punjab Institute of Medical Science Jalandhar, affiliated to Baba Farid University, Punjab, India, for her fruitful suggestions and motivational discussions regarding Physiology and Neurology. However, the EEG signals (publicly available) can be downloaded from a web page entitled with EEG, ERP and single cell recordings database by Quiroga [30, 31], on

Conflict of interest

Harish Kumar Garg and Amit Kumar Kohli declare that they have no conflict of interest in relation to the research work presented in this article.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringThapar UniversityPatialaIndia

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