ABSTRACT
Bowel sounds are one of the important physiological signals of the body,and different bowel sounds can reflect different gastrointestinal states.In this paper,long time bowel sound data is obtained with wearable full belly bowel sound recorder which is independent designed.After adaptive noise cancellation and wavelet threshold denoising,voice endpoint detection method based on short-time energy is used to identify effective bowel sounds.Experiments and results show that the sound recorder is simple and reliable.Through processing,analysis and endpoint detection algorithm,the recognition accuracy of effective bowel sounds is high,which has certain clinical practicality and research significance.
Subject(s)
Abdomen , Algorithms , Gastrointestinal Motility , Signal Processing, Computer-Assisted , SoundABSTRACT
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%.
ABSTRACT
Blending process, which is an essential part of the pharmaceutical preparation, has a direct influence on the homogeneity and stability of solid dosage forms. With the official release of Guidance for Industry PAT, online process analysis techniques have been more and more reported in the applications in blending process, but the research on endpoint detection algorithm is still in the initial stage. By progressively increasing the window size of moving block standard deviation (MBSD), a novel endpoint detection algorithm was proposed to extend the plain MBSD from off-line scenario to online scenario and used to determine the endpoint in the blending process of Chinese medicine dispensing granules. By online learning of window size tuning, the status changes of the materials in blending process were reflected in the calculation of standard deviation in a real-time manner. The proposed method was separately tested in the blending processes of dextrin and three other extracts of traditional Chinese medicine. All of the results have shown that as compared with traditional MBSD method, the window size changes according to the proposed MBSD method (progressively increasing the window size) could more clearly reflect the status changes of the materials in blending process, so it is suitable for online application.
ABSTRACT
Objective To find a useful index for real-time detecting of speech endpoint and improving the performance of speech processing under low SNR by analyzing fluctuation complexity of speech signals. Method The influence of state space partition method, window size and partition numbers on detecting performance was analyzed. The comparison experiments of speech signals corresponding to different SNR and noise type was designed using the measure of complexity behaviors based on the information gain.Result It was found that fluctuation complexity was more effective in detecting low-SNR speech than spectral entropy. Conclusion Fluctuation complexity is a valid feature to make speech/non-speech decision for the low SNR cases. The presented method can achieve robust performance and has a good real-time behavior.