http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

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

Time-Frequency Analysis.

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.

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

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Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains.

Automatic detection and classification of arrhythmias based on ECG signals are important to cardiac-disease diagnostics. The ability of the ECG classifier to identify arrhythmias accurately is based on the development of robust techniques for both feature extraction and classification. A classifier is developed based on using wavelet, transforms for extracting features and then using a radial basis function neural network (RBFNN) to classify the arrhythmia. Six energy descriptors are derived from the wavelent coefficients, over a single-beat interval from the ECG signal. Nine different continuous and discrete wavelet transforms, are considered for obtaining the feature vector. An RBFNN adapted to detect and classify life-threatening arrhythmias is then used to classify the feature vector. Classification results are based on 159 arrhythmia, files obtained from three different sources. Classification results indicate the potential for wavelet based energy descriptors to distinguish the main features of the signal and thereby enhance the classification scheme. The RBFNN classifier appears to be well suited to classifying the arrhythmia, owing to the feature vectors' linear inseparability, and tendency to cluster. Utilising the Daubechies wavelet transform, an overall correct classification of 97.5% is obtained, with 100% correct classification for both ventricular fibrillation and ventricular tachycardia.

Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias.

Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests

Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure

Time-frequency wavelet theory is used for the detection of life threatening electrocardiography (ECG) arrhythmias. This is achieved through the use of the raised cosine wavelet transform (RCWT). The RCWT is found to be useful in differentiating between ventricular fibrillation, ventricular tachycardia and atrial fibrillation. Ventricular fibrillation is characterised by continuous bands in the range of 2–10 Hz; ventricular tachycardia is characterised by two distinct bands: the first band in the range of 2–5 Hz and the second in the range of 6–8 Hz; and atrial fibrillation is determined by a low frequency band in the range of 0–5 Hz. A classification algorithm is developed to classify ECG records on the basis of the computation of three parameters defined in the time-frequency plane of the wavelet transform. Furthermore, the advantage of localising and separating ECG signals from high as well as intermediate frequencies is demonstrated. The above capabilities of the wavelet technique are supported by results obtained from ECG signals obtained from normal and abnormal subjects.

Detection of life-threatening cardiac arrhythmias using the wavelet transformation

Ventricular tachyarrhythmias, in particular ventricular fibrillation (VF), are the primary arrhythmic events in the majority of patients suffering from sudden cardiac death. Attention has focused upon these articular rhythms as it is recognized that prompt therapy can lead to a successful outcome. There has been considerable interest in analysis of the surface electrocardiogram (ECG) in VF centred on attempts to understand the pathophysiological processes occurring in sudden cardiac death, predicting the efficacy of therapy, and guiding the use of alternative or adjunct therapies to improve resuscitation success rates. Atrial fibrillation (AF) and ventricular tachycardia (VT) are other types of tachyarrhythmias that constitute a medical challenge. In this paper, a high order spectral analysis technique is suggested for quantitative analysis and classification of cardiac arrhythmias. The algorithm is based upon bispectral analysis techniques. The bispectrum is estimated using an autoregressive model, and the frequency support of the bispectrum is extracted as a quantitative measure to classify atrial and ventricular tachyarrhythmias. Results show a significant difference in the parameter values for different arrhythmias. Moreover, the bicoherency spectrum shows different bicoherency values for normal and tachycardia patients. In particular, the bicoherency indicates that phase coupling decreases as arrhythmia kicks in. The simplicity of the classification parameter and the obtained specificity and sensitivity of the classification scheme reveal the importance of higher order spectral analysis in the classification of life threatening arrhythmias. Further investigations and modification of the classification scheme could inherently improve the results of this technique and predict the instant of arrhythmia change.

Amjed S. Al-Fahoum

Papers

Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains.

Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias.

Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure

Detection of life-threatening cardiac arrhythmias using the wavelet transformation

A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques