Wavelet Transform Application in: Time Frequency Enhancement for Ventricular Late Potential Better Detection

Rahati, Saeed; Bajestani, G. Sadeghy; Falsoleiman, H.; HeidariBokavi, A.

doi:10.1109/bmei.2008.335

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The VLP component has been simulated with a sum of sine signals with a definite amplitude and frequency, added to the first milliseconds of the ST segment [10]:

\begin{matrix} aVLP [n] = \sum_{i = normal1}^{N} A_{i} \cos (normal2 π f_{i} n) . \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

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A Method for Ventricular Late Potentials Detection Using Time-Frequency Representation and Wavelet Denoising

Gadaleta

Giorgio

2012

ISRN Cardiology

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This study proposes a method for ventricular late potentials (VLPs) detection using time-frequency representation and wavelet denoising in high-resolution electrocardiography (HRECG). The analysis is performed both with the signal averaged electrocardiography (SAECG) and in real time. A comparison between the temporal and the time-frequency analysis is also reported. In the first analysis the standard parameters QRSd, LAS40, and RMS40 were used; in the second normalized energy in time-frequency domain was calculated. The algorithm was tested adding artificial VLPs to real ECGs.

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“…The VLP component has been simulated with a sum of sine signals with a definite amplitude and frequency, added to the first milliseconds of the ST segment [10]:

\begin{matrix} aVLP [n] = \sum_{i = normal1}^{N} A_{i} \cos (normal2 π f_{i} n) . \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

“…To test the method we used 60 real electrocardiographic signals with artificial VLP (aVLP). The VLP component has been simulated with a sum of sine signals with a definite amplitude and frequency, added to the first milliseconds of the ST segment [ 10 ]:

…”

Section: Resultsmentioning

confidence: 99%

A Method for Ventricular Late Potentials Detection Using Time-Frequency Representation and Wavelet Denoising

Gadaleta

Giorgio

2012

ISRN Cardiology

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Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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Wavelet Transform Application in: Time Frequency Enhancement for Ventricular Late Potential Better Detection

Cited by 2 publications

References 11 publications

A Method for Ventricular Late Potentials Detection Using Time-Frequency Representation and Wavelet Denoising

A Method for Ventricular Late Potentials Detection Using Time-Frequency Representation and Wavelet Denoising

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

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