Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
1.
Chinese Journal of Medical Instrumentation ; (6): 1-5, 2021.
Article in Chinese | WPRIM | ID: wpr-880412

ABSTRACT

The ECG signal is susceptible to interference from the external environment during the acquisition process, affecting the analysis and processing of the ECG signal. After the traditional soft-hard threshold function is processed, there is a defect that the signal quality is not high and the continuity at the threshold is poor. An improved threshold function wavelet denoising is proposed, which has better regulation and continuity, and effectively solves the shortcomings of traditional soft and hard threshold functions. The Matlab simulation is carried out through a large amount of data, and various processing methods are compared. The results show that the improved threshold function can improve the denoising effect and is superior to the traditional soft and hard threshold denoising.


Subject(s)
Algorithms , Computer Simulation , Electrocardiography , Signal Processing, Computer-Assisted , Wavelet Analysis
2.
Journal of Biomedical Engineering ; (6): 271-279, 2020.
Article in Chinese | WPRIM | ID: wpr-828170

ABSTRACT

Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.


Subject(s)
Algorithms , Microelectrodes , Principal Component Analysis , Reproducibility of Results , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio , Wavelet Analysis
3.
Chinese Journal of Medical Instrumentation ; (6): 90-93, 2019.
Article in Chinese | WPRIM | ID: wpr-772557

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 , Sound
4.
Chinese Journal of Medical Instrumentation ; (6): 318-321, 2019.
Article in Chinese | WPRIM | ID: wpr-772496

ABSTRACT

In order to diagnose and evaluate the human spinal lesions through the paravertebral muscles, a paravertebral muscle monitoring system based on surface EMG signals was designed. The system used surface mount electrodes to obtain the surface myoelectric signal (sEMG) of paravertebral muscle. The signal was filtered and amplified by the conditioning circuit. The signal was collected by the microcontroller NRF52832 and was sent to the mobile APP. After the signal was preprocessed by the wavelet threshold denoising algorithm in APP, the time and frequency characteristics of the sEMG signal reflecting the functional state of the muscle were extracted. The calculated characteristic parameters was displayed in real time in the application interface. The experimental results show that the system meets the design requirements in analog signal acquisition, digital processing of signals and calculation of characteristic parameters. The system has certain application value.


Subject(s)
Humans , Algorithms , Computers , Electrodes , Electromyography , Monitoring, Physiologic , Muscle, Skeletal , Signal Processing, Computer-Assisted
SELECTION OF CITATIONS
SEARCH DETAIL