ECG Fiducial Points Extraction by Extended Kalman Filtering

International audienceMost of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was constructed. Quantitative and qualitative evaluations of the proposed method have been done on Physionet QT database (QTDB). This method is also compared with a method based on Partially Collapsed Gibbs Sampler (PCGS). Results show that the proposed method can detect fiducial points of ECG precisely and mean of estimation error of all FPs (except Ton) do not exceed five samples (20 msec)

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

International audienceIn this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of-8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 msec and 22 msec, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods. Keywords: Electrocardiogram (ECG), Extended Kalman Filter (EKF), Dynamic Time Warping (DTW), Fiducial Point Extraction, Denoising

P Wave Detection in Pathological ECG Signals

Důležitou součástí hodnocení elektrokardiogramu (EKG) a následné detekce srdečních patologií, zejména v dlouhodobém monitorování, je detekce vln P. Výsledky detekce vln P umožňují získat ze záznamu EKG více informací o srdeční činnosti. Podle správně detekovaných pozic vln P je možné detekovat a odlišit patologie, které současné programy používané v medicínské praxi identifikovat neumožňují (např. atrioventrikulární blok 1., 2. a 3. stupně, cestující pacemaker, Wolffův-Parkinsonův-Whiteův syndrom). Tato dizertační práce představuje novou metodu detekce vln P v záznamech EKG během fyziologické a zejména patologické srdeční činnosti. Metoda je založena na fázorové transformaci, inovativních pravidlech detekce a identifikaci možných patologií zpřesňující detekci vln P. Dalším důležitým výsledkem práce je vytvoření dvou veřejně dostupných databází záznamů EKG s obsahem patologií a anotovanými vlnami P. Dizertační práce je rozdělena na teoretickou část a soubor publikací představující příspěvek autora v oblasti detekce vlny P.Accurate software for the P wave detection, mainly in long-term monitoring, is an important part of electrocardiogram (ECG) evaluation and subsequent cardiac pathological events detection. The results of P wave detection allow us to obtain more information from the ECG records. According to the correct P wave detection, it is possible to detect and distinguish cardiac pathologies which are nowadays automatically undetectable by commonly used software in medical practice (events e.g. atrioventricular block 1st, 2nd and 3rd degree, WPW syndrome, wandering pacemaker, etc.). This thesis introduces a new method for P wave detection in ECG signals during both physiological and pathological heart function. This novel method is based on a phasor transform, innovative rules, and identification of possible pathologies that improve P wave detection. An equally important part of the work is the creation of two publicly available databases of physiological and pathological ECG records with annotated P waves. The dissertation is divided into theoretical analysis and a set of publications representing the contribution of the author in the area of P wave detection.

ADAPTIVE MODELS-BASED CARDIAC SIGNALS ANALYSIS AND FEATURE EXTRACTION

Signal modeling and feature extraction are among the most crucial and important steps for stochastic signal processing. In this thesis, a general framework that employs adaptive model-based recursive Bayesian state estimation for signal processing and feature extraction is described. As a case study, the proposed framework is studied for the problem of cardiac signal analysis. The main objective is to improve the signal processing aspects of cardiac signals by developing new techniques based on adaptive modelling of electrocardiogram (ECG) wave-forms. Specially several novel and improved approaches to model-based ECG decomposition, waveform characterization and feature extraction are proposed and studied in detail. In the concept of ECG decomposition and wave-forms characterization, the main idea is to extend and improve the signal dynamical models (i.e. reducing the non-linearity of the state model with respect to previous solutions) while combining with Kalman smoother to increase the accuracy of the model in order to split the ECG signal into its waveform components, as it is proved that Kalman filter/smoother is an optimal estimator in minimum mean square error (MMSE) for linear dynamical systems. The framework is used for many real applications, such as: ECG components extraction, ST segment analysis (estimation of a possible marker of ventricular repolarization known as T/QRS ratio) and T-wave Alternans (TWA) detection, and its extension to many other applications is straightforward. Based on the proposed framework, a novel model to characterization of Atrial Fibrillation (AF) is presented which is more effective when compared with other methods proposed with the same aims. In this model, ventricular activity (VA) is represented by a sum of Gaussian kernels, while a sinusoidal model is employed for atrial activity (AA). This new model is able to track AA, VA and fibrillatory frequency simultaneously against other methods which try to analyze the atrial fibrillatory waves (f-waves) after VA cancellation. Furthermore we study a new ECG processing method for assessing the spatial dispersion of ventricular repolarization (SHVR) using V-index and a novel algorithm to estimate the index is presented, leading to more accurate estimates. The proposed algorithm was used to study the diagnostic and prognostic value of the V-index in patients with symptoms suggestive of Acute Myocardial Infraction (AMI)