Fast Parallelized Algorithm for ECG Analysis

A new approach based on the adoption of wavelet transforms is presented for the R point localization in ECG signals. The conceived real time signal processing technique, which uses a fast parallelized algorithm, has been evaluated adopting the standard MIT-BIH Arrhythmia database which includes specially selected holter recordings with anomalous but clinically important phenomena. In the procedure a soft thresholding technique is applied to dyadic scales in which the ECG signal is decomposed. Therefore, noise contribution is reduced and then signal is easily reconstructed in the time domain for further processing. Moreover, the tool analyzes the signal on different level wavelet representation at the same time showing a great parallelism degree and an enhancement in processing time. To evaluate the algorithm noise immunity, the MIT-BIH Noise Stress Test Database has been adopted containing baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The obtained performance shows the method validity in terms of algorithm speed up and characteristic parameter values. In fact, sensitivity and positive predictivity values of about 99.8% are obtained with a detection error rate of about 0.4%. Moreover, the conceived procedure gives satisfactory results also for ECG signals heavily corrupted by noise