A Kalman filter based methodology for EEG spike enhancement

Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece.
Computer Methods and Programs in Biomedicine (Impact Factor: 1.09). 03/2007; 85(2):101-8. DOI: 10.1016/j.cmpb.2006.10.003
Source: PubMed

ABSTRACT In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.


Available from: Alexandros T Tzallas, Jun 15, 2015
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    ABSTRACT: 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.
    Circuits Systems and Signal Processing 01/2015; DOI:10.1007/s00034-015-9982-y · 1.26 Impact Factor
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    ABSTRACT: In this chapter the Kalman Smoother, with or without the EM algorithm, has been used for the processing of the EEG signal in two cases, epileptic form spike identification and ERD/ERD analysis. Use of the Kalman Smoother forces to some simplifications of the model. This is performed in order to decrease the number of parameters which must be tuned. Based on the assumptions that the state transition matrix is the identity and the covariance is diagonal with the same element on the diagonal, there is one parameter to be tuned, the variance of the state noise. The value of this parameter defines how smooth or rough will be the evolution of states, in our case the TVAR coefficients. Large values of variance indicate rough estimates for the TVAR coefficients. This has as a result a noisy time varying spectrum. Small values indicated smooth estimates for the TVAR coefficients and hence a smooth time varying spectrum. The value of this parameter depends on the problem. In the case where we expect that the time varying spectrum is smooth, a small value for the variance of the state noise is preferable. However, the parameters can be estimated based on some optimization procedure like the EM algorithm. The EM algorithm provides with estimates of the parameters. So the tuning of the parameters is done automatically based on the dataset, without manual settings. This fact permits the use of full covariance for the state noise and a general transition state matrix. As a consequence the model is more flexible because of the different types of state noise. We observe that the Kalman Smoother with EM provides with smoother estimates than using the Kalman Smoother alone. This happens because the first approach can capture the patterns of the signal more accurately. In the estimation of the IF in the spike problem it is observed that the IF starts to increase before the appearance of the spike. Also, in the ERD/ERS analysis we observe that the IF is modulated when some events take place on the experiment, like the sound at t=2sec which denotes the beginning of the trial. In both problems we observe that the IF is a good measure to track changes in EEG activity.
    Kalman Filter Recent Advances and Applications, 04/2009; , ISBN: 978-953-307-000-1
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    ABSTRACT: While an electrode has allowed for simultaneously recording the activity of many neurons in microelectrode extracellular recording techniques, quantitative metrics of cluster quality after sorting to identify clusters suited for single unit analysis are lacking. In this paper, an objective measure based on the idea of neighborhood component analysis was described for evaluating cluster quality of spikes. The proposed method was tested with experimental and simulated extracellular recordings as well as compared to isolation distance and L ratio. The results of simulation and real data from the rodent primary visual cortex have shown that values of the proposed method were related to the accuracy of spike sorting, which could discriminate well- and poorly-separated clusters. It can apply on any study based on the activity of single neurons.
    The Open Biomedical Engineering Journal 09/2014; 8(1):60-7. DOI:10.2174/1874120701408010060