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In this study the performance of support vector machine (SVM)and back-propagation neural network were applied to analyze the classification of the electromyogram (EMG) signals obtained from normal, neuropathy and myopathy subjects. By using autoregressive (AR) modeling, AR coefficients were obtained from EMG signals. Moreover, the support vector machine and artificial neural network (ANN) were used as base classifiers. The AR coefficients were benefited as inputs for SVM and ANN. Besides, these coefficients were tested both in ANN and SVM. The results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with ANN. 相似文献
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Hardalaç F 《Journal of medical systems》2008,32(2):137-145
Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using
a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron
(MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters
of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that
92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from
neuro-fuzzy system. 相似文献
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In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients
are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as
EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions
by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop
(QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried
to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers
of hidden layer in the training process. This study shows that the artificial neural network increases the classification
performance using genetic algorithm. 相似文献
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In this work, a microcontroller-based EMG designed and tested on 40 patients. When the patients are in rest, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from right leg peroneal region. The histograms are constructed from the results of the FFT analysis. The analysis results shows that the amplitude of fibrillation potential of the muscle fiber of 30 patients measured from peroneal region is low and the duration is short. This is the reason why the motor nerves degenerated and 10 patients were found to be healthy. 相似文献
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研究对象为白天短时睡眠时记录下来的多导睡眠生理数据,主要是为了提取睡眠过程中出现的睡眠各阶段的特征,并实现自动分期。首先,同步采集了白天20~30 min的短时睡眠过程中的脑电图(EEG)等生理数据;然后利用快速傅里叶变换(FFT)对采集到的数据进行频谱分析,提取睡眠各阶段的频域特征;最后采用支持向量机对短时睡眠数据进行自动分期。实验结果表明:FFT结合支持向量机(SVM)在短时睡眠阶段的研究中能够得到较好的分期结果。因此,通过对短时睡眠过程中浅睡眠各阶段的特征和分类结果的分析,能够为短时睡眠提供客观评价的依据。 相似文献
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Cardiac Doppler signals recorded from aorta valve of 60 patients were transferred to a personal computer by using a 16 bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at jet blood flows such as cardiac Doppler signals, it sometimes causes wrong interpretation. In order to do a good interpretation and rapid diagnosis, cardiac Doppler blood flow signals were statistically arranged and then classified using neuro-fuzzy system. The NEFCLASS model, which is used to create a fuzzy classification system from data, was used. The classification results show that neuro-fuzzy system offers best results in the case of diagnosis. 相似文献
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Recently, numerous concealed information test (CIT) studies have been done with event related potential (ERP) for its sufficient validity in applied use. In this study, a new approach based on wavelet coefficients (WCs) and kernel learning algorithm is proposed to identify concealed information. Totally 16 subjects went through the designed CIT paradigm and the multichannel electroencephalogram (EEG) signals were recorded. Then, the high-dimensional WCs of ERP in delta, theta, alpha and beta rhythms were extracted. For the analysis of the data, kernel principle component analysis (KPCA) and a support vector machines (SVM) classifier are implemented. The results show that WCs features are significant differences between concealed information and irrelevant information (P?<?0.05). The KPCA is able to effectively reduce feature dimensionalities and increase generalization performance of SVM. A high accuracy (93.6%) in recognizing concealed information and irrelevant information is achieved, which indicates the combination KPCA and SVM may provide a useful tool for detecting the concealed information. 相似文献
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Background Fourier transform infrared spectroscopy (FT-IR) combined with chemometrics discriminant analysis technology could improve diagnosis. The present study aimed to evaluate the effects of FT-IR on malignant colon tissue samples in diagnosis of colon cancer.
Methods Principal component analysis (PCA) and support vector machine classification were used to discriminate FT-IR spectra from malignant and normal tissue. Colon tissues samples from 85 patients were used to demonstrate the procedure.
Results For this set of colon spectral data, the sensitivity and specificity of the support vector machine (SVM) classification were found both higher than 90%.
Conclusions FT-IR provided important information about cancerous tissue, which could be used to discriminate malignant from normal tissues. The combination of PCA and SVM classification indicated that FT-IR has a potential clinical application in diagnosis of colon cancer.
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