Abstract
Biomedical signal processing algorithms offer a variety of opportunities to improve performance. In this study, a new wavelet delta- function (WDF) method was proposed to effectively detect pathology from ECG signals. In this method, the delta function is determined for each value of the ECG signal. The coefficients 0, 1, and -1 are determined by summing this WDF. The level of detection of the difference between pathology and norm using data in the form of this vector allows changes in the diagnosis to be made more accurately than conventional methods. This is a new way of processing ECG signals by converting graphical data into vectors. The new method created a bar-code - like image. It has become possible to see myocardial infarction (MI), one of the diseases of the cardiovascular system, more effectively in real numbers of invisible peaks on the ECG. This allows for early detection and accurate diagnosis. The model we propose is an effective way to analyze an ECG. The WDF model we proposed can be used for early assessment of disease in biomedical signal processing.
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