Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi · Oct 2006
[The application of adaptive algorithm and wavelet transform in the filtering of ECG signal].
Electrocardiographic (ECG) signal are a kind of basic physiological signals of human body, and are very important in clinical diagnosis. But the ECG signals from body surface are often interfered by noises such as 50 Hz noise, baseline displacemant, electromyography (EMG) noise and edv. ⋯ To eliminate the ECG signals noises mentioned above,this paper adopts LMS adaptive algorithm and wavelet transform theory to design three kinds of digital adaptive filters-adaptive noise cancellation filter, wavelet transform filter and adaptive signal dividing filter to filter the corresponding noises. The results show that the three kinds of noises existing in the ECG signal have been efficiently eliminated.
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi · Oct 2006
[Dynamic analysis of heart rate variability based on wavelet transform].
The analysis of heart rate variability (HRV) has become a tool for noninvasively detecting the cardiovascular modulation of autonomic nervous system. Traditional analysis in frequency domain mainly includes calculating the power and detecting the peak frequency of each physiological frequency band. Whether employing the classical method or AR model to estimate the spectrum, the approximate stationarity of HRV is presupposed. ⋯ A dynamic analysis method based on wavelet transform was proposed in this paper, which not only can obtain the traditional indices in frequency domain, but can compute their dynamic values varying with time, called short-time power and short-time LF/HF ratio. The latter can dynamically evaluate the activity of autonomic nervous. Finally the method was applied to trace the balance of autonomic nervous in Atropin drag experiment.
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi · Oct 2006
[A multi-lead ECG classification network system based on modified LADT].
An electrocardiogram (ECG) classify system based on the features of the ECG and neural network classification, which is the simulation of the real world situation, was present. First, a modified approach of the linear approximation distance thresholding (LADT) algorithm was studied and the features of the ECG were obtained. ⋯ The algorithm was tested using several ECG signals of MIT-BIH, and the performance was good. The correct rate of the trained wave is 100%, untrained is 78.2%.