QRS complex processing system for telemetry — A comparative study based on filters

ECG records the electrical activity of the heart and is used to diagnose various cardiac disorders. One of the main targets of ECG analysis is the QRS complex that represents the excitation of the ventricles, the major pumps of the body. Therefore, accurate and reliable acquisition, processing and analysis of QRS complex is essential. Several filtering methods have been suggested for removal of noise such as baseline wandering and power line interference. Furthermore, various data compression schemes are available for ECG storage and telemetry applications such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). While the quality of reconstructed signal and compression ratio are poor in DCT, implementation of DWT is complex. Recently a new method for QRS complex compression and reconstruction had been proposed based on the Hermite polynomials that offers high compression ratio with less approximation error. In this work, three types of filtering methods namely median, Butterworth and using wavelet decomposition were applied on the QRS complex for preprocessing. Each of these preprocessed signals were compressed using Hermite functions. Comparing the quality of reconstructed QRS complex, we found that wavelet decomposition method offered a high quality reconstructed signal but at the cost of increased complexity of implementation while the signal quality was comparable in median and Butterworth filtering methods.