Characterising Chagas’ disease patients by means of ECG signals using wavelet transform

Abstract

An algorithm was constructed for ascertaining the characteristics of electrocardiograph (ECG) signals for detecting patients having abnormal heart rhythms (called arrhythmias)  and patients suffering chronic Chagas’ disease from the Meta department and recordings for healthy people, using the Symlets 7 (sym 7) wavelet as an alternative form of Fourier analysis for this type of signal. A database was created containing high resolution ECG of people from the region suffering heart disease and chronic Chagas’ disease who were informed about the study and voluntarily accepted taking part in it; all bioethical recommendations were followed. The ECG recordings were taken in the Universidad de Los Llanos’ facilities using high resolution equipment (derivation D II).  This algorithm led to the most important characteristics of each ECG recording being identified for patients having abnormal heart rhythms and Chagas’ disease. The PR, QT, QTc, RR and QRS-complex intervals were measured, as were wave heights (amplitude) and heart rate with greater than 98% precision, thereby making this approach a useful tool for obtaining a better diagnosis for symptoms of cardiac abnormalities and of chronic Chagas’ disease.

Key words: wavelet analysis; QRS complex;  electrophysiology.