Automated classification of ECG patterns is facilitated by careful selection of waveform features This paper presents a method for evaluating the properties of features that describe the shape of a QRS complex By examining the distances in the feature space for a class of nearly similar complexes, shape transitions which are poorly described by the feature under investigation can be readily identified To obtain a continuous range of waveforms, which is required by the method, a mathematical model is used to simulate the QRS complexes

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A Method for Evaluation of QRS Shape Features Using a Mathematical Model for the ECG

This report describes eight patients with B-cell chronic lymphocytic leukemia whose disease became more aggressive over a variable period of time. This clinical progression was associated with a change in cell morphology from small lymphocytes to an increasing number of large transformed lymphocytes in the blood, bone marrow, and lymph nodes. In the peripheral blood, the predominant large cell was a prolymphocyte. The small lymphocytes and the prolymphocytes had identical cell surface markers in each patient. However, the prolymphocytes had a greater density of surface immunoglobulin than did the same lymphocytes. No features were found that help predict in which patients CLL will convert to a more aggressive form. Once transformation has taken place, however, there appears to be a correlation between the number of prolymphocytes in the blood and patient survival. It is suggested that the entities of prolymphocytic transformation of CLL, prolymphocytic leukemia, and Richter's syndrome are less distinct than has been thought previously. These disorders probably represent several phases of transformation of the same cell type, and they may be examples of different stages in the natural history of CLL.

Prolymphocytic transformation of chronic lymphocytic leukemia.

A complete solution for the delineation of the ECG signal into its component waves is proposed from a system theoretic point of view. The discrete cosine transform (DCT) of a bell-shaped biphasic function is approximated mathematically by a system function with two poles and two zeros, i.e., of order

Analysis of ECG from pole-zero models

An encoding and classification method is provided which is capable of comprehensively featuring relevant spectral information for the early detection of diabetes-induced cardiac autonomic neuropathy using the LP-cepstral discriminant classifier for quick, noninvasive assessment (screening) of neuropathy in diabetics Patients are tested in the supine position thus avoiding the stresses to the patient that are associated with tilt table testing

Classification of heart rate variability patterns in diabetics using cepstral analysis

This study presents a novel approach to modeling the electrocardiogram (ECG): the Gaussian pulse decomposition. Constituent waves of the ECG are decomposed into and represented by Gaussian pulses using an iterative algorithm: the chip away decomposition (ChAD) algorithm. At each iteration, a nonlinear minimization method is used to fit a portion of the ECG waveform with a single Gaussian pulse, which is then subtracted from the ECG waveform. The process iterates on the resulting residual waveform until the normalized mean square error is below an acceptable level. Three different minimization methods were compared for their applicability to the ChAD algorithm; the Nelder-Mead simplex method was found to be more noise-tolerant than the Newton-Raphson method or the steepest descent method. Using morphologically different ECG waveforms from the MIT-BIH arrhythmia database, it was demonstrated that the ChAD algorithm is capable of modeling not only normal beats, but also abnormal beats, including those exhibiting a depressed ST segment, bundle branch block, and premature ventricular contraction. An analytical expression for the spectral contributions of the constituent waves was also derived to characterize the ECG waveform in the frequency domain. The Gaussian pulse model, providing an intuitive representation of the ECG constituent waves by use of a small set of meaningful parameters, should be useful for various purposes of ECG signal processing, including signal representation and pattern recognition.

Gaussian pulse decomposition : An intuitive model of electrocardiogram waveforms

Homomorphic analysis and pole-zero modeling of electrocardiogram (ECG) signals are presented in this paper. Four typical ECG signals are considered and deconvolved into their minimum and maximum phase components through cepstral filtering, with a view to study the possibility of more efficient feature selection from the component signals for diagnostic purposes. The complex cepstra of the signals are linearly filtered to extract the basic wavelet and the excitation function. The ECG signals are, in general, mixed phase and hence, exponential weighting is done to aid deconvolution of the signals. The basic wavelet for normal ECG approximates the action potential of the muscle fiber of the heart and the excitation function corresponds to the excitation pattern of the heart muscles during a cardiac cycle. The ECG signals and their components are pole-zero modeled and the pole-zero pattern of the models can give a clue to classify the normal and abnormal signals. Besides, storing only the parameters of the model can result in a data reduction of more than 3:1 for normal signals sampled at a moderate 128 samples/s.

Homomorphic Analysis and Modeling of ECG Signals

https://ieeexplore.ieee.org/iel5/10/4123058/04123063.pdf

A. K. Goyal

Papers

Homomorphic Analysis and Modeling of ECG Signals

Homomorphic Analysis andModeling ofECGSignals