The microsoft DirectX technique is utilized to achieve the display of fast moving waveforms with high resolution and full screen in the environment of WindowsXP. The waveforms can move fluently under the resolution of 1280 x 1024 with the fastest speed of 1 m/s without any dither.
Signal Processing, Computer-Assisted ; Software

According to the biomedical signal characteristics, a new biomedical signal processing toolkit is developed. The toolkit is programmed by Java. It is used in basic digital signal processing, random signal processing and etc. All the methods in toolkit has been tested, the program is robust. The feature of the toolkit is detailed explained, easy use and good practicability.
Signal Processing, Computer-Assisted ; Software

OBJECTIVEDue to random sampling of non-adaptive, high-quality reconstruction of the original signal, one-dimensional non-stationary multi-channel EEG signal can be achieved automatic detection and analysis.

METHODSA new multicomponent redundant dictionaries with the atoms of the Gaussian function and its first and second derivatives was built in the paper, and reconstructed signal base on compressed sensing measurement model.

RESULTSThe selected dictionary atoms can more effectively match the EEG signals in a variety of transient characteristics of the waveform, allowing the formation of EEG signal is more sparse matching pursuit decomposition. With the theory based on compressed sensing signal sampling, only half of the original signal with different sample size can be used to reconstruct the original signal quality, the important instantaneous features of the waveform can well be maintained.

CONCLUSIONSignal sampling based on the theory of compressed sensing contains enough information of the original signal, using the prior conditions of EEG signals (or compressibility) sparsity, high-dimensional signal and original image can be reconstructed through a certain decoding of linear or nonlinear model.

By analysing the f(a) singularity spectra of the ST segments of the synchronous 12-lead ECG, we have found that the singularity spectrum is close to monofractality and its area is only half the area of the synchronous 12-lead ECG f(alpha) singularity spectrum. The ST segments of the synchronous 12-lead ECG signal also has f(alpha) singularity spectra distribution and it also has a reasonable varying scope. We have also found that the lead number of the ST segment f (alpha) singularity spectra for adults having coronary heart disease overstep the reasonable scope tends to increase over that of the ECG f(alpha) singularity spectra. These findings show that using the ST segments f(alpha) singularity spectra distribution of the synchronous 12-lead ECG is more effective than using the synchronous 12-lead ECG on the clinical analysis.
Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

Anesthesia plays an essential role in clinical operations. Guiding anesthesia by EEG signals is one of the most promising methods at present and it has obtained good results. The analysis and process of the EEG signals in anesthesia can provide clean signal for further research. This paper used variance threshold method to remove the mutation fast and large interfering signals; and used notch filter to remove frequency interference, smoothing filter to remove baseline drift and Butterworth low-pass filter to remove high frequency noise at the same time. In addition to this, the translation invariant wavelet method to remove interference noise on the signals which was after the classical filter and retained non-stationary characteristics was used to evaluate parameter calculation. By comparing the calculated parameters from treated signal using this paper's methods and untreated signal and standard signal, the standard deviation and correlation has been improved, particularly the major parameters BetaR, which provides better signal for integration of multi-parameter to evaluate depth of anesthesia index for the latter.
Anesthesia ; methods ; Humans ; Signal Processing, Computer-Assisted

We proposed a research of a heart sound envelope extraction system in this paper. The system was implemented on LabVIEW based on the Hilbert-Huang transform (HHT). We firstly used the sound card to collect the heart sound, and then implemented the complete system program of signal acquisition, pretreatment and envelope extraction on LabVIEW based on the theory of HHT. Finally, we used a case to prove that the system could collect heart sound, preprocess and extract the envelope easily. The system was better to retain and show the characteristics of heart sound envelope, and its program and methods were important to other researches, such as those on the vibration and voice, etc.
Heart Sounds ; Humans ; Signal Processing, Computer-Assisted

The existing analog reordering and folding technology has the following problems: it cause the attenuation of the ultrasonic signal, and it is difficult to achieve beam steering in color Doppler ultrasonic diagnostic instrument. This article proposes a design method to achieve digital reordering of dynamic focusing data. The digital reordering is composed of two parts, bit reordering which is implemented with multiplier and byte reordering using switch selection. The results show that it can meet the design requirements using fewer resources.
Image Enhancement ; Signal Processing, Computer-Assisted ; Ultrasonics

This article is to introduce the dramatic improvements in efficiency, sensitivity and bandwidth of the ultrasound transducers--PureWave Crystal Technology.
Signal Processing, Computer-Assisted ; Transducers ; Ultrasonics ; instrumentation

In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.
Algorithms ; Humans ; Phonocardiography ; Signal Processing, Computer-Assisted

The basic theory of Higher Order Spectral Analysis and the most generally used Bispectrum are introduced in the paper. By certain experiments of EEG signal acquisition and bispectrum analysis, it is showed that the Higher Order Spectrum has an advantage over power spectrum, which is based on Second Order Statistics, in processing non-linear signal and restraining Gauss noise signal.
Electroencephalography ; methods ; Signal Processing, Computer-Assisted