Search

Electrocardiogram (ECG) signal processing and analysis provide crucial information about functional status of the heart. The QRS complex represents the most important component within the ECG signal. Its detection is the first step of all kinds of automatic feature extraction. QRS detector must be able to detect a large number of different QRS morphologies. This paper examines the use of wavelet detail coefficients for the accurate detection of different QRS morphologies in ECG. Our method is based on the power spectrum of QRS complexes in different energy levels since it differs from normal beats to abnormal ones. This property is used to discriminate between true beats (normal and abnormal) and false beats. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.64% and a positive predictivity of 99.82%., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

In this paper, we introduce a new system for ECG beat classification using support vector machines classifier with a double hinge loss. The proposed classifier rejects samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the consequence of a wrong classification can be so harmful that it is convenient to reject such sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds according to the cost of rejecting or misclassifying a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrythmia database. The achieved results are represented by the error reject tradeoff. We obtained 98.2% of sensitivity with no rejection and more than 99% of sensitivity for the optimal classification cost being competitive to other published studies. [ABSTRACT FROM AUTHOR]

This paper describes a method for the classification of heartbeats based on electrocardiogram QRS complex and T wave autoregressive (AR) features and interbeat distance (RR interval) features. The QRS complex (length n samples) is modelled using AR model of order P. A second AR model of order P1 is applied to the electrocardiogram segment including ST segment and T wave (ST/T segment) with a length of n1 samples. In addition, RR interval features such as the distance between one heartbeat and its preceding (pre-RR) and following (post-RR) heartbeats are extracted. The use of AR modelling is motivated by the fact that QRS complex and ST/T segment corresponding to normal beats present different patterns (in shapes and amplitudes) than those corresponding to ventricular ectopic beats. Lengths and AR model orders that minimize the number of misclassifications are determined. Obtained features are used to train and test support vector machine classifier. The proposed method is tested on MIT/BIH Arrhythmia Database and it is compared to state of the art methods. Overall classification accuracy reached 97.02%, whereas it reached 98.86% in the case of subject-specific scheme. Results are better or comparable to those obtained by state of the art methods. [ABSTRACT FROM AUTHOR]