Electrocardiogram based identification using a new effective intelligent selection of fused features

Abstract

Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task.

Feature extraction of electrocardiogram signal using machine learning classification

“…The geometric and physiological characteristics of heart produces different signals and maintain uniqueness for different subjects [1]. The ECG signals of every individual person produce unique patterns and characteristics where the heart condition of a patient is decided by the outline of the waveform where function and rhythm of the heart produces effective information for the detection of heart disease [2,3]. Each of the cardiac cycle of the ECG signal generates the characteristic wave of P-QRS-T.…”