Evaluating arrhythmias in ECG signals using wavelet transforms

Addison, Paul; Watson, James N.; Clegg, Gareth; Hölzer, Michael; Sterz, F.; Robertson, Colin E.

doi:10.1109/51.870237

Cited by 94 publications

(79 citation statements)

References 26 publications

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“…We provide a short description of both the WT and the OMP algorithm. An extensive description of both methods is available in references [1,2,18].…”

Section: Analysis Methodsmentioning

confidence: 99%

“…For acute events, the Wavelet transform (WT) could be better suited due to its properties of representing signals with a finite number of sudden short duration and time localized changes that differ from the baseline harmonics [1,2,11,18]. Other methods using advanced digital signal processing methodologies, like the orthogonal matching pursuit (OMP) algorithm [19] and the basis pursuit algorithm [7,22], merge several different analysis methods to create a so-called over-complete dictionary depicting the different elements of the signal so to find an optimal representation of the biological signal.…”

Section: Introductionmentioning

confidence: 99%

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Heart rate analysis by sparse representation for acute pain detection

Tejman‐Yarden

Levi

Beizerov

et al. 2015

Med Biol Eng Comput

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Engineering. This e-offprint issampled for 5-min baseline, followed by a cold pressor test (CPT). Analysis was done by the WT and the OMP algorithm with a Fourier/Wavelet dictionary separately. Data from 11 subjects were analyzed. Compared to baseline, The WT analysis showed a significant coefficients' density increase during the pain incline period (p < 0.01) and the entire CPT (p < 0.01), with significantly higher coefficient amplitudes. The OMP analysis showed a significant wavelet coefficients' density increase during pain incline and decline periods (p < 0.01, p < 0.05) and the entire CPT (p < 0.001), with suggestive higher amplitudes. Comparison of both methods showed that during the baseline there was a significant reduction in wavelet coefficient density using the OMP algorithm (p < 0.001). Analysis by the twoway ANOVA with repeated measures showed a significant proportional increase in wavelet coefficients during the incline period and the entire CPT using the OMP algorithm (p < 0.01). Both methods provided accurate and nondelayed detection of pain events. Statistical analysis proved the OMP to be by far more specific allowing the Fourier coefficients to represent the signal's basic harmonics and the wavelet coefficients to focus on the time-specific painful event. This is an initial study using OMP for pain detection; further studies need to prove the efficiency of this system in different settings.

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“…We provide a short description of both the WT and the OMP algorithm. An extensive description of both methods is available in references [1,2,18].…”

Section: Analysis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heart rate analysis by sparse representation for acute pain detection

Tejman‐Yarden

Levi

Beizerov

et al. 2015

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

“…This transformation of the ECG signals has been carried out in the past using techniques such as autocorrelation function, time frequency analysis, and wavelet transforms (WT) (Maglaveras, Stamkopoulos et al 1998;Addison, Watson et al 2000;Kundu, Nasipuri et al 2000;Dokur and Olmez 2001;Saxena, Kumar et al 2002). Results of these and other studies in the literature have demonstrated that WT is the most promising method to extract features that characterize the behavior of ECG signals in an effective manner.…”

Section: An Improved Procedures For Detection Of Heart Arrhythmiasmentioning

confidence: 99%

Application of Computational Intelligence Techniques for Cardiovascular Diagnostics

Nataraj¹,

Jalali²,

Ghorbanian³

2012

The Cardiovascular System - Physiology, Diagnostics and Clinical Implications

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“…As applied in a medical context, a conditional probability is the likelihood of some conclusion, C, given some evidence/observation, E, where a dependence relationship exists between C and E. This probability is denoted as P(C| E) where (1) Bayes' theorem is the method of finding the converse probability of the conditional, (2) This conditional relationship allows an investigator to gain probability information about either C or E with the known outcome of the other. Now consider a complex problem with n binary variables, where the relationships among them are not known for the purpose of predicting a single class output variable (e.g., node 1 in Figure 6).…”

Section: Bayesian Network Structure Discoverymentioning

confidence: 99%

“…Trained physicians are able to recognize patterns in a patient's ECG signal and use them as the basis for diagnosis [1], for instance to diagnose heart ailments such as arrhythmia [2], ischemia [3,4], or prediction of an impending heart attack [5]. Researchers have tried since the inception of computers to develop techniques and algorithms for automated processing of ECG signals for various medical applications [6,7], whether as standalone applications or as a decision aid to physicians.…”

Section: Introductionmentioning

confidence: 99%

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Wiggins

Saad

Litt

et al. 2008

Applied Soft Computing

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Objective-To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers.Methods and Material-We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA).Results and Conclusions-The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

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Evaluating arrhythmias in ECG signals using wavelet transforms

Cited by 94 publications

References 26 publications

Heart rate analysis by sparse representation for acute pain detection

Heart rate analysis by sparse representation for acute pain detection

Application of Computational Intelligence Techniques for Cardiovascular Diagnostics

Evolving a Bayesian classifier for ECG-based age classification in medical applications

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