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.

Download full-text


Available from: Alexandros T Tzallas,
1 Follower
57 Reads
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    The Open Electrical & Electronic Engineering Journal 08/2015; 2(4):59-67.
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Denoising is an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications. For this purpose, Blind Source Separation (BSS) methods, such as Independent Component Analysis (ICA) and Decorrelated and Colored Component Analysis (DCCA), are commonly used. Although ICA and DCCA-based methods are powerful tools to extract sources of interest, the procedure of eliminating the effect of sources of non-interest is usually manual. It should be noted that some methods for automatic selection of artifact sources after BSS methods exist, although they imply a training supervised step. On the other hand, in cases where there are some a priori information about the subspace of interest, semi-blind source separation methods can be used to denoise EEG signals. Among them the Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) are two well-known semi-blind frameworks that can be used with a priori information on the subspace of interest. In this paper, we compare the ICA and DCCA-based methods, namely CoM2 and SOBI, respectively, with GEVD and DSS in the application of extracting the epileptic activity from noisy interictal EEG data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. The comparison of these four methods in terms of performance and numerical complexity shows that CoM2 give better performance for very low SNR values but require visual inspection to select the sources of interest. For higher SNR values, GEVD and DSS based approaches give similar results but with lower numerical complexity and without requiring a visual selection of the sources of interest.
    IRBM 11/2014; 36(1). DOI:10.1016/j.irbm.2014.10.002 · 0.52 Impact Factor
  • Source
    • "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]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
    The Scientific World Journal 08/2014; 2014:973063. DOI:10.1155/2014/973063 · 1.73 Impact Factor
Show more