In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

A wavelet-based ECG delineator: evaluation on standard databases

The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

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The principles of software QRS detection

The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

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Wavelet transforms and the ECG: a review.

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application

ECG signal analysis through hidden Markov models

QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

The rapid and objective measurement of timing intervals of the electrocardiogram (ECG) by automated systems is superior to the subjective assessment of ECG morphology. The timing interval measurements are usually made from the onset to the termination of any component of the EGG, after accurate detection of the QRS complex. This article describes a real-time system that uses wavelet transforms to overcome the limitations of other methods of detecting QRS and the onsets and offsets of P- and T-waves. Wavelet transformation is briefly discussed, and detection methods and hardware and software aspects of the system are presented, as well as experimental results.

Using wavelet transforms for ECG characterization. An on-line digital signal processing system

A novel algorithm for ST-segment analysis is developed using the multi-resolution wavelet approach. The system detects the QRS complexes and analyses each beat using the wavelet transform to identify the characteristic points (fiducial points). These fiducial points are, iso-electric level, the J point, and onsets and offsets of the QRS complex and T wave. The algorithm determines the T onset by looking for a point of inflection between the J point and the T peak. Furthermore, detection of characteristic points by the wavelet technique reduces the effect of noise. The results show that the proposed approach gives very accurate ST levels, as compared to the conventional (empirical) technique, at higher heart rates and with different morphologies. The algorithm detects the ST-segment length in 92.3% beats with an error of 4 ms, and in 97.3% beats the error is within 8 ms. The algorithm has been implemented on a TMS320C25 based add-on DSP card connected to a PC to provide the on-line analysis and display of ST-segment data.

Wavelet based ST-segment analysis.

Timing characterisation of the ECG using wavelet transforms is a new technique in which multiscale analysis reduces the influence of noise. This technique issued to investigate the effect of noise and to estimate the errors involved in the detection of onsets and offsets of ECG waves. With appropriate choice of scales of analysis the study shows that the errors involved in the measurement of QRS width in the presence of base-line wander are negligible. The 50 Hz power-line interference introduces a maximum error of 6.25% if it is greater than 50% of the signal amplitude. The P and T complexes are not affected by power-line interference, but the base-line wander introduces a maximum error of 9.6%. In situations with the simultaneous presence of both types of noise, the use of an optimised scale restricts the errors to within clinically acceptable limits.

Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets

The present work attempts to resolve some of the problems encountered in QT interval analysis and to describe a new algorithm that uses wavelets to analyze the QT interval on a beat-by-beat basis. The QT interval is further segmented into its contributing factors to find the variability in the repolarization phase. The technique and the algorithm have been tested on control and disease data from the standard MIT ECG library to prove the reproducibility and accuracy of the system. The technique has been implemented on a PC DSP add-on card to do the analysis on a beat-by-beat basis.

An automated approach to beat-by-beat QT-interval analysis

A novel algorithm for detection and analysis of QT interval, a risk factor for sudden cardiac death, is developed using the multiresolution wavelet approach. The characteristic points for detection of QT interval, i.e., the onset and offsets of the QRS complex and the T wave are detected by analyzing the wavelet transform of the ECG at particular scales. The results of the detailed study using standard data base indicate that proposed technique can be used to monitor critical heart patients for localization of problems in the duration of ventricular activation. The algorithm has been implemented on TMS320C25 based add-on DSP card to PC to provide the beat by beat analysis and display of QT interval data.

J. S. Sahambi

Papers

Using wavelet transforms for ECG characterization. An on-line digital signal processing system

Wavelet based ST-segment analysis.

Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets

An automated approach to beat-by-beat QT-interval analysis

Beat by beat QT interval detection and characterization.