To enhance speech recognition, as well as Mandarin tone recognition in noice, we proposed a speech coding strategy called zero-crossing of fine structure in low frequency (LFFS) for cochlear implant based on low frequency non-uniform sampling (LFFS for short). In the range of frequency perceived boundary of human ear, we used zero-crossing time of the fine structure to generate the stimulus pulse sequences based on the frequency selection rule. Acoustic simulation results showed that although on quiet background the performance of LFFS was similar to continuous interleaved sampling (CIS), on the noise background the performance of LFFS in Chinese tones, words and sentences were significantly better than CIS. In addition to this, we also got better Mandarin recognition factors distribution by using the improved index distribution model. LFFS contains more tonal information which was able to effectively improve Mandarin recognition of the cochlear implant.
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.
Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.