Objective To reduce information loss in the refining processing of photoplethysmographic (PPG) pulse waveform. Method A novel PPG pulse waveform refining processing technique, named truncation/extrapolation technique was proposed. The key point of the proposed technique was to preserve the shape pattern of the most significant parts of the waveform at the expense of the distortion of the insignificant part. Thus, as compared with the conventional time-normalization method, the proposed technique ensured the accumulation coherent of the signal energy could be. Result Waveform analysis and comparison of experimental data between two methods verified the effectiveness of the proposed method in maintaining the most significant time domain features of the pulse waveform over a relatively wide range of the heart rate change, especially the shape and width of the dominant peak and the position of the dicrotic notch of the first derivative of PPG(dPPG). Conclusion The proposed method reduces the information loss in PPG pulse waveform refining processing, thus helps further processing of the PPG signal using some advanced signal processing techniques, such as waveform modeling and pattern recognition.

This paper reviews the concept and structure of Virtual Instruments and the application of VB6.0 to Virtual Instruments. As an example, an acquisition and procession apparatus of EEG signals based on VB6.0 and virtual instruments is introduced in detail.

Anesthesia as a necessary procedure in the process of surgical operation could restrain the response of patients to the damage stimulation; However, improper anesthesia could also result in severe misfortune for patients. At the present time, one kind of monitor technology assuring highly effectual anesthesia is exigently required in clinical practice and many researchers have actively undertaken investigations to seek the parameters predicting the depth of anesthesia (DOA). Electroencephalogram (EEG) assumes a dominant position in the current researches on detecting the depth of anesthesia. In this paper, the achievements of detecting the depth of anesthesia by means of EEG are systematically reviewed and the potentials are anticipated.
Anesthesia ; Anesthesiology ; methods ; Electroencephalography ; Humans ; Monitoring, Physiologic

In this paper, an image mosaic algorithm based on feature points was proposed by making a study of the image mosaic methods and the characteristics of pathological image. Points of interest were firstly extracted by Moravec operator in this method, and the corresponding feature matches of the original images were achieved by correlation coefficient. The parameters of affine transformation were calculated by the matching feature points, and weighted average method was used to fuse images. The experimental results show that, as for the pathological image with translation and rotation, the algorithm can extract the matching feature points accurately and realize the image seamless mosaic.
Algorithms ; Image Enhancement ; methods ; Image Processing, Computer-Assisted ; methods ; Pathology ; methods ; Pattern Recognition, Automated ; methods

By using the fuzzy min-max neural network, the quantitative structure-activity relationship (QSAR) of mutagenicity is studied. With the established QSAR model, the mutagenicity is predicted and the results showed that QASR is superior to linear-regression model. Further discussion on the models and the results is presented in this paper.
Algorithms ; Cluster Analysis ; Fuzzy Logic ; Models, Chemical ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship

By use of the complexity analysis indices (approximate entropy, complexity), the heart rate variability signals obtained from 38 subjects' ECG during anesthesia were analyzed. The results showed that there is an obvious chaos change of heart rate variability during anesthesia, both the complexity and approximate entropy of heart rate variability during anesthesia are evidently less than those during consciousness (P<0.05). In this dissertation, we decompose the heart rate variability during anesthesia into 1/f part and non-fractal part, and then analyze these parts. The results reveal that the sensitivity of the complexity indices can be improved by decomposing the heart rate variability.
Adult ; Anesthesia ; Autonomic Nervous System ; drug effects ; Electrocardiography ; Entropy ; Heart Rate ; physiology ; Humans ; Middle Aged ; Models, Statistical ; Signal Processing, Computer-Assisted

To implement the network printing in PACS (Picture Archiving and Communication System), each level of DICOM (Digital Imaging and Communication in Medicine) print management service is defined by means of object oriented method, and the corresponding events and properties are implemented with C++ language. It is tested on laser printers of AGFA, KODAK and FUJI to determine whether the network environment supports the print management service and implements the presentation LUT (Look Up Table) correctly. The quality control of printing at the level of data flow is also assessed. The program finally succeeds in realizing the network printing based on DICOM in hospital environment.
Computer Communication Networks ; instrumentation ; Image Processing, Computer-Assisted ; standards ; Medical Records Systems, Computerized ; organization & administration ; standards ; Printing ; methods ; Radiology Information Systems ; standards

In accordance with the character of heart rate variability(HRV) signal, we have divided HRV signal into 1/f fractal component and 1/f non-fractal component by use of wavelet transform. This division will be beneficial to the acqusition of the character of HRV signal as well as to the quantitative analysis of 1/f fractal component.
Algorithms ; Electrocardiography ; Heart Rate ; physiology ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted

By use of fractal analysis indexes-correlation dimension, fractal dimension and scaling exponent, the heart rate variability signals obtained from 38 subjects' ECG during anesthesia are analyzed. The results show that there is an obvious change of fractal characteristic of heart rate variability during anesthesia. The correlation dimension (P < 0.000001) during anesthesia is evidently less than that during consciousness, while the short-range scaling exponent a (P < 0.0001) during consciousness is evidently less than that during anesthesia. These illustrate that the difference in fractal characteristic between anesthesia and well-balanced state can be detected by the fractal analysis of heart rate variability. In the end, the paper poses that the analysis of heart rate variability is fit for monitoring the depth of anesthesia by detrended fluctuation analysis.
Anesthesia ; Electrocardiography ; Heart Rate ; physiology ; Humans ; Monitoring, Physiologic ; methods