Usefulness of the 12‐Lead ECG to Identify Epicardial Ventricular Substrate and Epicardial Ventricular Tachycardia Site of Origin

Bazán, Víctor; Marchlinski, Francis E.

doi:10.1002/9781119152637.ch81

Cited by 1 publication

(2 citation statements)

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“…We write the result to the previously The value of ecg_points_all_blacks is a twodimensional matrix in which the first level is the line where the black points were. This line contains the coordinates of the Y axis for each black point, which have the following form: [12,26], [12,34], [18,23,32,40], 41, 46, 46, 46, 46, 46, 46, 46, where on the first line here are two black points with the coordinates 12 and 26; on the second line there are two black points with the coordinates 12 and 34. There are a number of black points like in [15][16][17].…”

Section: Building the Algorithm Of Image Processing In The Pythonmentioning

confidence: 99%

“…From the above methods, it follows that for more accurate determining the arrhythmia signals for data processing, it is necessary to have highquality ECG signals that can be obtained directly when measured on a patient. However, unfortunately, highly accurate and sensitive equipment is not always available or is not available locally due to the high cost and needed maintenance [18].…”

Section: Introductionmentioning

confidence: 99%

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Methods of extracting electrocardiograms from electronic signals and images in the Python environment

Zholmagambetova¹,

Mazakov²,

Jomartova³

et al. 2020

Diagnostyka

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High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.

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