ST Segment Change Detection by Means of Wavelets

Glišović, Nataša; Petrović, Aleksandar

doi:10.1109/neurel.2006.341196

Cited by 9 publications

(18 citation statements)

References 4 publications

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“…In practice, QRS complex is usually considered to be symmetrical, while T wave is less so. However, it has been shown, PR and ST points can be estimated using biorthogonal wavelets under the assumption of QRS complex and T wave symmetry [2]. Moreover, having as an aim proposed quantitative analysis, it is quite plausible to suppose basic QRS complex and T wave symmetry [4].…”

Section: Methodsmentioning

confidence: 99%

“…Hence used in this work. The filter coefficients of both the symmetric low pass (LP) H and the antisymmetric high pass (HP) filters G and K are given in Table 1 [2]. Decomposition and reconstruction filters satisfy further equations:…”

Section: Methodsmentioning

confidence: 99%

“…An initial downward deflection after the P-wave is termed as, 'Q', the dominant upward deflection is the 'R' and the terminal part is denoted as 'S'. The T-wave represents ventricular recovery or repolarization [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Detection ischemic episodes from electrocardiogram signal using wavelet transform

Moridani

Pouladian²

2009

JBiSE

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In this paper, we propose an algorithm for de-tection of myocardial ischemic episodes from electrocardiogram (ECG) signal using the wavelet transform technique. The algorithm was tested on data from the European ST-T change database. Results show that this algorithm is effective for distinguishing normal ECGs from ischemic. We developed a method that uses wavelets for extracting ECG patterns that are characteristic for myocardial ischemia

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Detection ischemic episodes from electrocardiogram signal using wavelet transform

Moridani

Pouladian²

2009

JBiSE

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“…Adaptive application of the method [4]- [6]. Wavelet transform has been an interesting solution to ECG signal preprocessing [7]- [9]. Although this method is promising, the scale and the thresholds for non-stationary baseline drift removals and noise depression cannot be chosen adaptively.…”

Section: Introductionmentioning

confidence: 99%

An ECG signal processing algorithm based on removal of wave deflections in time domain

Kim

Won

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper introduces a new approach to process biomedical signals by surgically removing wave deflections in time domain. The method first determines the epochs of high frequency deflections, cuts out them from the signal, and then connects the two disconnected points. To determine the epoch of a deflection to be removed, four slope trace waves are used to isolate the deflection based on signal characteristics of amplitude, slope, duration, and distance from neighboring deflections. The method has been applied to simulated data and MIT-BIH arrhythmia database to show its practical efficacy in the case of baseline wandering removal. It is found that the method has the capability to identify and remove high frequency deflections appropriately, leaving low frequency deflection such as baseline drifting.

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“…In the first, each cardiac beat is labeled as normal or ischemic and, in the second, sequential ischemic beats are appropriately grouped in order to identify ischemic episodes. In this context, several methodologies have been developed, such as: time and / or frequency domain analysis techniques [4][5], wavelet transform [6] [7], artificial neural networks [8][9] [10], principal component analysis / Karhunen-Loève transform [11][12] [13], discrete Hermite functions [14], rule based systems [15][16] and fuzzy systems [17] [18]. In the present work a new methodology for ischemic episodes automatic detection is proposed, considering ST segment deviation, T wave and QRS morphology variations.…”

mentioning

confidence: 99%

Ischemia detection in the context of a cardiovascular status assessment tool

Rocha

Paredes

Carvalho

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this work a new strategy for ischemic episodes automatic detection is proposed, considering ST segment deviation and T wave and QRS morphology characteristics. A new measure of ST deviation based on time-frequency analysis, and the use of the expansion in Hermite functions technique for T wave and QRS complex morphology characterization, are the key points of the proposed methodology. HeartCycle is a European project that aims to improve life quality of coronary artery disease (CAD) and heart failure (HF) patients. Within this project, the Medical Risk Assessment module is responsible for develop models to assess cardiovascular (CV) risk and status of referred patients. The present work was performed under the context of CV status models, where myocardial ischemia plays a central role. For algorithms validation purposes, the European Society of Cardiology (ESC) ST-T database was used. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.

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ST Segment Change Detection by Means of Wavelets

Cited by 9 publications

References 4 publications

Detection ischemic episodes from electrocardiogram signal using wavelet transform

Detection ischemic episodes from electrocardiogram signal using wavelet transform

An ECG signal processing algorithm based on removal of wave deflections in time domain

Ischemia detection in the context of a cardiovascular status assessment tool

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