Biomedical subjects
Publications and source records attributed to Zhiqian Ye.
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.
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.
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 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.
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.
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.