Redefining Performance Evaluation Tools for Real-Time QRS Complex Classification Systems

International audienceIn a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. This paper aims to redefine classical performance evaluation tools in entire QRS complex classification systems and to evaluate the effects induced by QRS detection errors on the performance of a heartbeat classification processing (normal vs abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system imposed of the classical detector proposed by Hamilton & Tompkins, followed by a neural network classifier. This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds an increase of about 50% bad classifications

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Clasificación de latidos según estándar AAMI mediante Red Neuronal sobre plataforma Intel Edison

En la actualidad el desarrollo de dispositivos “wearables” y la tecnología “para llevar encima” son uno pilares de las grandes empresas del sector electrónico. Al amparo del gran desarrollo de los sistemas electrónicos y de las conexiones inalámbricas, como el WiFi, se están diseñando e investigando por todo el mundo hoy en día cientos de aplicaciones para aplicar la tecnología “wearable” que nos permiten tener un reloj inteligente en la muñeca o enviar un email de camino al trabajo mientras viajamos en tren, hasta utilizar estos dispositivos en campos en los que aún no han desarrollado todo su potencial como, por ejemplo, el entorno biomédico. Este Trabajo de Fin de Grado consiste en aplicar las posibilidades de los dispositivos “wearables” en dos aplicaciones biomédicas: un clasificador de latidos mediante una Red Neuronal y el cálculo del ritmo cardíaco mediante el algoritmo de detección de latidos Pan-Tompkins diseñados en Matlab. Para la primera aplicación se utilizarán las Redes Neuronales Artificiales, un tipo de computación que aún no ha desarrollado todo su potencial y que está en constante evolución, así como las enormes bases de datos de electrocardiogramas del sitio web Physionet. Para la segunda aplicación se utilizará el algoritmo de detección de complejos QRS (latidos) de Pan- Tompkins, uno de los programas más robustos y eficaces en este campo. Después ambas aplicaciones serán implementadas sobre la plataforma Intel Edison, un Pc del tamaño de una tarjeta SD, y se visualizarán los resultados por el ordenador. La finalidad del sistema es doble, por un lado diseñar las aplicaciones comentadas, y por otro el proyecto pretende, que gracias a la implementación de esas aplicaciones en la Intel Edison, la plataforma demuestre tener la suficiente memoria y capacidad de cálculo para ser la base en futuros proyectos en los que se diseñe una aplicación biomédica “wearable” que permita desde la recogida de datos de un ECG, hasta su procesamiento y clasificación de latidos, en un dispositivo que podemos llevar encima sin apenas notarlo.Ingeniería Electrónica Industrial y Automátic

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)