Data processing in electrocardiographs by wavelet transformation for early forecasting of parossysmal arthritis

Abstract

ECG analysis is widely used to diagnose many heart diseases, which are the leading cause of death in different countries. The quality of the ECG signal can be affected and worsened by various sources, such as the patient’s condition, basic walk, electrocardiogram contact, and others. In addition, if the ECG is visually monitored, the probability of human error is high, each 10-result is interpreted with an error (Brikena Xhaja, 2015: 305-312) And also for many ECG images it is simply not possible to conduct a visual analysis of the frequency data of the signal. The morphology of low-amplitude high-frequency signals, the so-called P waves, hides valuable information for early preclinical disease prediction. That is, the need to search for new methods of early preclinical diagnosis is still relevant. Since the majority of clinically useful information in the ECG is found in the intervals and amplitudes determined by its significant points (characteristic peaks and wave boundaries), the development of accurate and reliable methods for automatic ECG delineation is a matter of great importance, especially for the analysis of long records (Juan Pablo Martinez, 2014: 570-581). The problems of retrieving information from the electrophysiological signal that can not be obtained by visual analysis of the recording, as well as the problems of automation of traditional algorithms of medical analysis are relevant in connection with the lack of research in this field. The aim of the research is to search for new areas of application of the wavelet transform method in signal processing. Wavelet transformation, obtained widely in 2000 in the study of signal properties, allows us to "discern"hidden frequency-time signal data with the help of approximating and detailing coefficients. The obtained results show that the proposed algorithm provides real efficiency in the processing of primary signals for the task of isolating the detailing coefficients of the ECG signal. Our study shows that Morlet’s wavelet analysis of P intervals, which can be applied easily and inexpensively, can reliably predict the incidence of symptomatic episodes of paroxysmal atrial fibrillation in patients without clinically and echocardiographically expressed heart disease. Wavelet analysis can contribute to our understanding of the electrophysiological mechanisms underlying the generation and recurrence of paroxysmal atrial fibrillation and can identify patients at high risk of increased relapses of paroxysmal atrial fibrillation, thereby creating the prospect of early application of non-invasive and invasive therapeutic strategies to prevent future events of paroxysmal ciliary arrhythmias.

Keywords

• electrocardiogram
• wavelet transformation
• paroxysmal atrial fibrillation