In this work, a new method of heart sound signal preprocessing is presented. First, the heart sound signals are decomposed by using multilayer wavelet transform. And then double parameters as thresholds are used in processing each layer after decomposition for denoising. Next, reconstruction of heart sound signals could be done after processing last layer. Four methods, i.e. wavelet transform, Hilbert-Huang transform (HHT), mathematical morphology, and normalized average Shannon energy, were used to extract the envelop of the heart sound signals respectively after reconstruction of heart sounds. All methods were improved in this study. We finally in our study chose 30 cases of raw heart sound signals, which were selected randomly from a database comed from The Clinical Medicine Institute of Montreal, and processed them by using the improved methods. The results were satisfactory. It showed that the extracted envelope with the original signal has a high degree of matching, whether it is a low frequency portion or high frequency portion. Most of all information of heart sound has been maintained in the envelope.
Extraction uterine contraction signal from abdominal uterine electromyogram (EMG) signal is considered as the most promising method to replace the traditional tocodynamometer (TOCO) for detecting uterine contractions activity. The traditional root mean square (RMS) algorithm has only some limited values in canceling the impulsive noise. In our study, an improved algorithm for uterine EMG envelope extraction was proposed to overcome the problem. Firstly, in our experiment, zero-crossing detection method was used to separate the burst of uterine electrical activity from the raw uterine EMG signal. After processing the separated signals by employing two filtering windows which have different width, we used the traditional RMS algorithm to extract uterus EMG envelope. To assess the performance of the algorithm, the improved algorithm was compared with two existing intensity of uterine electromyogram (IEMG) extraction algorithms. The results showed that the improved algorithm was better than the traditional ones in eliminating impulsive noise present in the uterine EMG signal. The measurement sensitivity and positive predictive value (PPV) of the improved algorithm were 0.952 and 0.922, respectively, which were not only significantly higher than the corresponding values (0.859 and 0.847) of the first comparison algorithm, but also higher than the values (0.928 and 0.877) of the second comparison algorithm. Thus the new method is reliable and effective.