Discrete wavelet transform-based ECG features detection and extraction using low-cost ADS1293EVM

Most biomedical research has focused on the study of ECG signals to diagnose heart or chronic diseases such as diabetes and hypertension, among others. Most heart-related diseases are identified by changes in the morphology of the waves and complexes that form the ECG signals, however, automated techniques for detecting ECG signal features result in expensive equipment. In this article, the authors present automatic feature detection and extraction techniques in real ECG signals using low-cost platforms and algorithms based on the discrete wavelet transform. The platform used to acquire ECG signals is the ADS1293EVM acquisition card and the MATLAB software is used to compute these signals. The developed program is evaluated with 30 ECG recordings of 6 seconds, having results in the detection of characteristics of interest greater than 90%.