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: 1.9). 03/2007; 85(2):101-8. DOI: 10.1016/j.cmpb.2006.10.003
Source: PubMed


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.

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Available from: Alexandros T Tzallas
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    • "Many approaches have been proposed to identify time-varying autoregressive TVAR models. While traditional adaptive parameter estimation algorithms, for example the recursive least squares (RLS) and least mean squares (LMS) can be applied to track time-varying trends [15]–[16], these algorithms can often produce lagged tracking of time varying parameters. Fast transversal recursive instrumental variable (FTRIV) and generalized least mean squares (GLMS) [17], are proposed for the estimation of AR non Gaussian processes. "
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    ABSTRACT: In this paper, we present an adaptive spectrum estimation method for non-stationary Biomedical Signals. The algorithm is based on time-varying autoregressive (TVAR) modeling where the time varying parameters are estimated by Kalman filtering. The algorithm generates adaptively an estimate of the power spectral density (PSD) at each time instant. A comparison was made with the recursive least squares (RLS) method, the main feature of the proposed approach is the capability of the Kalman filter that enables tracking smooth and sharp changes in the time varying process parameters. Furthermore, it provides better time-frequency resolution and gives a good spectral peak matching. Simulation studies and applications on real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of non-stationary biomedical processes.
    Full-text · Article · Aug 2015 · The Open Electrical & Electronic Engineering Journal
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    • "They may also be categorized by the features they used: morphological features [28] or time–frequency ones [22]. Most of the spike detection methods have an enhancement stage that generates an output signal in which the distinction between the spikes and the noise is increased by some filtering methods such as Wavelet Transform [29] [27], matched filters [30] or Kalman filter [31]. At this stage, the output signal is used in a decision procedure in order to extract the spike peak times. "
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    • "The general framework of peak detection algorithm usually involves several processes which are signal preprocessing, peak candidate detection, feature extraction, and classification. Various signal preprocessing methods have been employed such as data compression [19], wavelet transform [6], Kalman filter [20], and Hilbert transform [15]. Two methods for peak candidate detection have been used which are three point sliding window method [8] and k-point nonlinear energy operator (k-NEO) method [21]. "
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