A Multi Rate Marginalized Particle Extended Kalman Filter for P and T Wave Segmentation in ECG Signa

A Multi Rate Marginalized Particle Extended Kalman Filter for P and T Wave Segmentation in ECG Signals

Abstract: The marginalized particle extended Kalman filter (MP-EKF) has been known as an effective model-based nonlinear Bayesian framework in the field of electrocardiogram (ECG) signal denoising. In this paper, we reveal another potential capability of MP-EKF and propose a multi rate MP-EKF based framework for P and T wave segmentation in ECG signals. The proposed multi rate implementation of MP-EKF leads to better estimation of states and avoids unwanted errors in estimation procedure. The behavior of particles in the multi rate MP-EKF is controlled by a novel particle weighting strategy that helps the particles adapt themselves with respect to ECG signal trajectory. After ECG filtering, a novel morphology based algorithm uses the estimates of multi rate MPEKF to determine the P and T wave fiducial points. This algorithm is a combination of well-known morphological operators such as ‘opening’, closing, ‘top-hat’ and ‘bottom-hat’ transforms. The segmentation performance of the proposed algorithm was evaluated on QT database and it showed promising results in comparison to other Bayesian frameworks such as partially collapsed Gibbs sampler (PCGS) and extended Kalman filter (EKF).

Existing system: Automatic ECG segmentation is an important part of automatic ECG analysis. The aim of automatic ECG segmentation is to determine as accurately as possible the start and endpoints of ECG feature waveforms and their morphologies, especially their peak amplitudes. Many methods including low pass differentiation (LPD) , hidden Markov models (HMMs), wavelet transform (WT), support vector machine (SVM) , empirical mode decomposition (EMD) have been proposed for ECG segmentation. Additionally, Bayesian frameworks such as extended Kalman filter (EKF) , and partially collapsed Gibbs sampler (PCGS) have been used for ECG segmentation and fiducial point (FP) extraction. In it was shown that the performance of Bayesian PCGS was comparable to other algorithms such as WT and LPD. These encouraging results motivated other researchers to further investigate the application of Bayesian approaches for ECG segmentation.

Proposed system: Recently, a Bayesian model-based nonlinear framework called marginalized particle extended Kalman filter (MP-EKF) was proposed that was able in denoising various kinds of stationary and non-stationary interferences present in ECG. This framework used a modified version of nonlinear ECG dynamical model (EDM) proposed by McSharry et al. To denoise ECG signals in various noisy environments and in the presence of arrhythmias. The efficiency of this nonlinear framework depends on two factors: Selection of a proper EDM, and selection of a suitable particle weighting strategy. The obtained results demonstrated the superiority of MP-EKF over conventional algorithms as well as model-based methods in ECG signal denoising.

Advantages: Additionally, Bayesian frameworks such as extended Kalman filter (EKF) and partially collapsed Gibbs sampler (PCGS) have been used for ECG segmentation and fiducial point (FP) extraction.

In it was shown that the performance of Bayesian PCGS was comparable to other algorithms such as WT and LPD. This framework used a modified version of

nonlinear ECG dynamical model (EDM) proposed by McSharry et al. To denoise ECG signals in various noisy environments and in the presence of arrhythmias.

Disadvantages: The efficiency of this nonlinear framework depends on two factors: Selection of a proper EDM, and selection of a suitable particle weighting strategy. The obtained results demonstrated the superiority of MP-EKF over conventional algorithms as well as model-based methods in ECG signal denoising.

MP-EKF are used by a post processing algorithm that is based on mathematical morphology. This algorithm can accurately delineate the peaks, start and endpoints of various kinds P and T waves including the inverted ones.

Modules: Electrocardiagram segmentation: The electrocardiogram (ECG) signal is a powerful clinical tool that provides a noninvasive insight into the heart’s activity and its diagnostic characteristics. A typical ECG is composed of pseudo-periodic waveforms or cycles referred to as heartbeats. Each heartbeat is comprised of specific waveforms: P wave, QRS complex and T wave. Additionally, a small U wave is occasionally present after T wave. Each waveform corresponds to either depolarization or repolarization of muscle cells in certain regions of the heart. The cardiac cycles starts with P wave (the start and endpoints of which are called and) which is related to atrial depolarization in the heart. The P wave is followed by QRS complex which is related to ventricular depolarization. The QRS complexes are the most distinguishable feature waveforms in ECG. The QRS complex is followed by T wave which corresponds to ventricular repolarization. Assuming the absence of U wave, the endpoint of T wave represents the end of a cardiac cycle.

Kalman filter: Recently, a Bayesian model-based nonlinear framework called marginalized particle extended Kalman filter (MP-EKF) was proposed that was able in denoising various kinds of stationary and non-stationary interferences present in ECG. This framework used a modified version of nonlinear ECG dynamical model (EDM)

proposed by McSharry et al. To denoise ECG signals in various noisy environments and in the presence of arrhythmias. The efficiency of this nonlinear framework depends on two factors: Selection of a proper EDM, and selection of a suitable particle weighting strategy. The obtained results demonstrated the superiority of MP-EKF over conventional algorithms as well as model-based methods in ECG signal denoising.

ECG Segmentation: Morphological operators are well-known in the field of signal and image processing. They need simple implementation techniques and exhibit robust and adaptive performance in extracting shape information even in nonlinear nonstationary environments . Structuring elements are the basis of mathematical morphology. The shape of a structuring element and its size determines which specific shape information of a signal can be extracted. There are two basic morphological operators: erosion (⊝) and dilation (⨁). Many other complex morphological operators such as opening (⊚) and closing (⊙) can be viewed as combinations of erosions and dilations. Suppose that uv_ is a one dimensional discrete signal with length of N. Also assume that L"v_ is a one dimensional structuring elements with length of M (w < _). The morphological erosion, dilation, opening and closing are defined.

Model based filtering: In this paper, a novel method is proposed for P and T wave segmentation in ECG signals using multi-rate MP-EKF and mathematical morphology. This is the first study which demonstrates MP-EKF’s potential capability in ECG segmentation.

The modified EDM in the proposed MP-EKF allows it to trace ECG feature parameters better while the novel particle weighting strategy inside the MP-EKF regulates the behaviors of particles with respect to ECG signal trajectory. The modified EDM in this paper is a mixed linear/nonlinear state space model and multi-rate implementation of MP-EKF, helps it to estimate the linear states more accurately. The morphological post-processing algorithm uses the outputs of MPEKF to accurately delineate the peaks, onsets and offsets of P and T waves. The proposed algorithm was evaluated on QT database and compared with the PCGS algorithm introduced and EKF based algorithm proposed. The results showed that

the multi-rate MP-EKF performs better and more reliably in various kinds of ECG morphologies in comparison to PCGS and EKF. The power of multi-rate MP-EKF lies in its nonlinear framework, particle weighing strategy and multi-rate state estimation.