Body Surface ECG Signal Shape Dispersion

Khaddoumi, Balkine; Rix, Hervé; Meste, Olivier; Fereniec, M.; Maniewski, Roman

doi:10.1109/tbme.2006.877121

Cited by 4 publications

(6 citation statements)

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“…We notice that using the difference shape approach, better classification is obtained. In fact, the surface method depends on the shape but it doesn't exploit the whole information about shape as the difference in shape [2].…”

Section: Resultsmentioning

confidence: 99%

“…2 coefs , is given as the projection of a healthy subject coefficient on the data base of sick subjects.…”

Section: Methodsmentioning

confidence: 99%

“…These data comes from 15 healthy subjects and 12 patients. In previous works [1,2] the two groups were separated using the shape variability of the T-wave in each column of electrodes, using a shape difference approach. The aim of this paper is to present the same work using a different approach and to compare it to the previous one.…”

Section: Introductionmentioning

confidence: 99%

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Spatial shape variability analysis of T-waves in the ECG signal

Mabrouki

Khaddoumi

Sayadi

2013

2013 International Conference on Electrical Engineering and Software Applications

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Abstract-This work aims to study the shape variability of the T-waves using multi channel records. The classification between a group of subjects with Myocardial Infarction (MI) and a group of healthy subjects is obtained using a synthetic signal called Integral Shape Averaging (ISA). Our methodology is based on calculation of projection coefficients corresponding to each electrode for both groups of subjects. These coefficients represent a distance between each subject and one of the groups. This latter allows us to compare each subject to each population. These coefficients are obtained by a surface difference between the signal of each electrode in each column and the associated ISA signal. In addition, we seek for the electrode that represents the best separation between the two populations. In this study we improve previous studies and make a comparison with another approach which is a combination between the ISA technique and the calculation of shape difference.

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Section: Resultsmentioning

confidence: 99%

“…2 coefs , is given as the projection of a healthy subject coefficient on the data base of sick subjects.…”

Section: Methodsmentioning

confidence: 99%

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Spatial shape variability analysis of T-waves in the ECG signal

Mabrouki

Khaddoumi

Sayadi

2013

2013 International Conference on Electrical Engineering and Software Applications

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“…For detailed evaluation of the signal shape changes within the QRS complex the Distribution Function Method (DFM) [5] was used, which previously has been shown as a useful tool in detection of small variation in shape of chromatographic peaks [6] as well as in cardiac diagnosis [7,8].…”

Section: Methodsmentioning

confidence: 99%

Exercise induced depolarization changes in BSPMs for assessment of ischemic heart disease

Kania¹,

Maniewski²,

Zaczek³

et al. 2015

2015 Computing in Cardiology Conference (CinC)

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“…Both temporal and spatial distribution of the electrical potentials generated by the heart on the surface of the body can be investigated by high resolution Body Surface Potential Mapping (BSPM) [7]- [9]. BSPM is the most promising method of extending medical utility of ECG.…”

Section: Introductionmentioning

confidence: 99%

Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps

Janusek¹,

Kania²,

Zaczek³

et al. 2011

Measurement Science Review

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T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant Twave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected.

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Body Surface ECG Signal Shape Dispersion

Cited by 4 publications

References 0 publications

Spatial shape variability analysis of T-waves in the ECG signal

Spatial shape variability analysis of T-waves in the ECG signal

Exercise induced depolarization changes in BSPMs for assessment of ischemic heart disease

Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps

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