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

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

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.

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

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

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

In this paper, we using Mexican-hat wavelet transform to detect characteristic points of ECG signal based on the characteristic points corresponding with the extremes of Mexican-hat wavelet transform. It offers a new detection method of ECG signal analysis. This method is simple and it is proved to be accurate and reliable. The correct rate of QRS detection rate examined by the MIT-BIT arrhythmia database rises up to 99.9%.
Algorithms ; Electrocardiography ; Humans ; Signal Processing, Computer-Assisted

In order to deal with the disadvantages in ECG waveform detection by wavelet harr transform at level 3, we put forward a new set of algorithm which combines wavelet transform (WT) and morphological peak and valley detection. The combination algorithm can make up the limitation in detecting ECG swing by WT and improve the accuracy of ECG waveform detection effectively.
Algorithms ; Electrocardiography ; Humans ; Signal Processing, Computer-Assisted

This article add mode control module to the phased-array using digital sampling technology which achieved a phase accuracy of 3.75 degrees, making phase, phased signal duration, interval for continuous signal and repeat times pre-configured before the out put of phased signal, to realize the precise timing of phased signal. Experiments indicate that the output of phased signal modulated by mode control can reach the timing precision of 0.48 micros and will not affect the original phase control function t. In addition it can realize intermittent therapy mode which can reduce the injury of non-focal areas caused by ultrasound energy.
Equipment Design ; Signal Processing, Computer-Assisted ; Transducers

In an ECG auto-analysis system, correct QRS detection is most important. For this detection there are several methods, such as derivative-based algorithms, filter-bank methods and wavelet based methods and neural network approaches, but there is no single method that is extensively used. These methods are compared and analyzed in this paper.
Algorithms ; Electrocardiography ; Humans ; Signal Processing, Computer-Assisted