Compressão de sinais ECG utilizando DWT com quantização não-linear e por sub-bandas

With the increasing development of biomedical devices technology, there is more access to bioelectrical signals. That allows great advances in reaching diagnostics, planning treatments and monitoring patients. Particularly, the electrocardiogram (ECG) has been used for many purposes. Besides that, simple and low-cost ways to acquire the ECG have been found. Nevertheless, those advances require the improvement of the ECG signal coding processes, in a way that allows its efficient storage and transmission in terms of memory requirements and energy consumption. In this context, this dissertation proposes two contributions. Firstly, it presents an ECG signal compression algorithm, using wavelet transforms, and proposing a novel quantization process, not found in the literature. In said process, the transformation is done using the discrete wavelet transform (DWT) and the quantization consists of a non-linear re-ordering of the transformed coefficients magnitudes (gamma correction) in tandem with a sub-band quantization. The second contribution consists in a systematic study of the performance of the different wavelet families through the results obtained by the proposed algorithm, also calculating the optimum quantization parameters for each wavelet family. For the analysis of these methods, tests were done evaluating the performance of the proposed algorithm, comparing its results with other methods presented in the literature. In said tests, signals from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital database (MIT-BIH) were used as reference. A part of the database was utilized to optimize the parameters of each wavelet family, and the final performance was evaluated with the remaining signals from the database. Specifically, for signal 117 of the MIT-BIH database, which is the most used signal to compare results in the literature, the proposed method led to a compression factor (CR) of 11,40 and a percentage root-mean-square difference (PRD) of 1,38. It was demonstrated that the algorithm generates better compression results when compared to the majority of state-of-the-art methods. The simplicity of the algorithm’s implementation also stands out in relation to other algorithms found in the literature.