The paper reports a kind of new compression method of ECG signal. The method is realized by means of wavelet packets transform based on best basis performed by Shanon-Weaker entropy criterion. The result of simulation shows that this is an efficient compression method characterized by larger compression ratio and less loss, and the original signal can be recovered well.
Algorithms ; Data Compression ; Electrocardiography ; Entropy ; Signal Processing, Computer-Assisted ; Wavelet Analysis

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.
Algorithms ; Computer Simulation ; Electrocardiography ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Now researches of ECG data compression mainly focus on compressing the ECG data of low sampling rate. A BW-based high sampling rate ECG data lossless compression algorithm is proposed in this paper. We apply difference operation to the original ECG data first and take part of the 16-bit binary differential value as 8-bit binary. Then the differential results are coded with the move-to-front coding method in order to make the same characters centralizing in a certain area. Last, we gain a high compression ratio by using the arithmetic coding method further. Our experimental results indicate that this is an efficient lossless compression method suitable for body surface ECG data as well as for heart ECG data. The average compression ratios come up to 3.547 and 3.608, respectively. By comparison with current ECG compression algorithms, our algorithm has gained much improvement in terms of the compression ratio, especially when applied to the high sampling rate ECG data.
Algorithms ; Data Compression ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

In this paper is proposed a new method that approximates line segment with angle to control line as a basis for improving radial fitting. Experiments on selected records from the Massachusettes Institute of Technology and Boston's Beth Isral Hospital (MIT-BIH) arrhythmia database have revealed that the improved algorithm not only increases computation quantity, but also improves approximating quality and potentiates Real-time application of the linear approximation distance thresholding (LADT).
Algorithms ; Data Compression ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

In order to choose a fast and efficient real-time method in beta wave information extraction, we compared the result and the efficiency of the information separation of both fast Fourier transform (FFT) and wavelet transform of EEG beta band in the present paper. Our work provides the basis for the EEG data come from the real-time health assessment of 3DTV. We took the EEGs of 5 healthy volunteers before, after and during the process of watching 3DTV and meanwhile recorded the results. The trends of the relative energy and the time cost of two methods were compared by using both the FFT and wavelet packet transform (WPT) which was to extract the feature of EEG beta wave. It demonstrated that (1) Results of the two methods were consistent in the trends of watching 3DTV; (2) Results of the differences in two methods were consistent before and after watching 3DTV; (3) FFT took less time than the wavelet transform in the same case. It is concluded that the results of both FFT and Wavelet transform are consistent in feature extraction of EEG, and a fast method to work with the large quantities of EEG data obtained in the experiments can be offered in the future.
Algorithms ; Brain ; physiology ; Electroencephalography ; methods ; Fourier Analysis ; Humans ; Male ; Signal Processing, Computer-Assisted ; Television ; Wavelet Analysis ; Young Adult

Biomedical signal analysis often needs to separate the trend component from the non-trend component to achieve different purposes and applications in signal analysis. This article introduces three kinds of detrending nonlinear component methods used in the process of biomedical signal analysis: wavelet analysis, empirical mode decomposition, smoothness priors approach as well as the application in the separation of the actual biomedical data. The different separation methods should be selected according to different research goals as well as the feature of the signal.
Algorithms ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted ; Wavelet Analysis

This paper carries out the multi-scale decomposition of pulse signals on different frequency bands by wavelet transform, and the analysis of power spectral density shows that the detailed signal energy of the fourth layer mainly concentrates on the 3-5 Hz, while the energy of the fifth layer concentrating on the 1-3 Hz. Through calculating the energy, the experimental results show that: the hypertension signal energy of 3-5 Hz band increases significantly, compared with the normal signals.
Hypertension ; physiopathology ; Signal Processing, Computer-Assisted ; Wavelet Analysis

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.
Algorithms ; Heart Sounds ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Surface electromyogram (sEMG) may have low signal to noise ratios. An adaptive wavelet thresholding technique was developed in this study to remove noise contamination from sEMG signals. Compared with convention- al wavelet thresholding methods, the adaptive approach can adjust thresholds based on different signal to noise ratios of the processed signal, thus effectively removing noise contamination and reducing distortion of the EMG signal. The advantage of the developed adaptive thresholding method was demonstrated using simulated and experimental sEMG recordings.
Algorithms ; Electromyography ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis

A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal.
Algorithms ; Electrocardiography ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis