A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals

Abstract

This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.

Stochastic approximation driven particle swarm optimization with simultaneous perturbation – Who will guide the guide?

“…Particularly, we can foresee that those problems where a proper guidance mechanism such as FGBF is needed but cannot be applied due to infeasibility problems (e.g. evolutionary ANN applications by the standalone MD-PSO as in [18] and [23]), SAD MD-PSO would be a promising solution to further improve the performance. For those multi-objective problems (MOPs), the application of SAD PSO can be crucial since the goal is to search for a set of Pareto-optimal solutions and hence, the particles must follow (a set of) best guide(s) that will lead them toward the optimal solutions.…”

Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach

“…Further details and an extensive set of experiments demonstrating the optimality of the networks evolved with respect to several benchmark problems can be found in [17] and [25].…”

Integrated Information System of Monitoring and Management for Heart Centers

“…This software component is implemented under the form of a desktop application that runs on one or several computers inside the monitoring centre [3][4][5][6][7][8]. By means of this application, the specialist doctor has access to the medical record data, the current ECG investigation as well as to the history of all ECG investigations made for the patient in the past, no matter the place where these were realized (another family doctor).…”