Biomedical Image Processing / Medical Image Processing
Amirhossein Chalechale; Ali Khadem
Volume 14, Issue 1 , May 2020, , Pages 31-42
Abstract
The well-timed and correct diagnosis of Bipolar Disorder (BD) followed by proper treatment is vital for avoiding the progress of the illness. Although using resting-state functional magnetic resonance imaging (rs-fMRI) data and the features extracted from them may have an important role in diagnosing ...
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The well-timed and correct diagnosis of Bipolar Disorder (BD) followed by proper treatment is vital for avoiding the progress of the illness. Although using resting-state functional magnetic resonance imaging (rs-fMRI) data and the features extracted from them may have an important role in diagnosing this kind of brain disorder, few researches have been conducted on this illness and the obtained results are not accurate. In this research we used a new approach to diagnose BD I. By using seed-based correlation we used the following 4 regions of interest in order to extract the connectivity maps for each subject: the posterior cingulate cortex (PCC) to probe the default mode network (DMN), the amygdala and the subgenual cingulate cortex (sgACC) to probe the salience network (SN) and the dorsolateral prefrontal cortex (dlPFC) to probe the frontoparietal network (FPN). After computing the connectivity maps for each subject we extracted the most important connectivities using different threshold on the t-value from the t-test that we applied on them and then we used a support vector machine (SVM) using only four combined features and a leave one out cross-validation (LOOCV) method to classify the two groups. The proposed method was done on rs-fMRI data from 49 healthy control subjects and 34 BD I patients and an accuracy of higher than 90% was obtained in differentiating the two groups from each other. Also there were no hyper-connectivity between the 4 ROIs and the rest of the brain regions for the BD I groups in relation with the healthy controls. The regions that had most of the hypo-connectivity with the 4 ROI’s that we used were: the angular gyrus (Ag) and the orbitofrontal cortex (OFC) with the PCC, the anterior cingulate cortex with the amygdala and the dlPFC and the inferior temporal gyrus (ITG) with the sgACC.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Hasan Moradi; Bahador Makki Abadi
Volume 2, Issue 2 , June 2008, , Pages 141-154
Abstract
Hish rate classification of Electromyogram (EMG) signals for controlling of prosthetic hands is still a hot topic among the rehabilitation research titles. Specially, when the degree of freedom in artificial hands increases, the classification rate decreases dramatically. In this paper, a new five layer ...
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Hish rate classification of Electromyogram (EMG) signals for controlling of prosthetic hands is still a hot topic among the rehabilitation research titles. Specially, when the degree of freedom in artificial hands increases, the classification rate decreases dramatically. In this paper, a new five layer classifier based on Neuro-Fuzzy-Genetic structure was introduced to increase the classification accuracy of EMG signals. The proposed classifier has a self- organized structure, which adaptively creates new rules according to the input features and trains the fuzzy rule weights based on the back propagation method. Finally, the genetic algorithm (GA) was employed for the final tuning stage. In this study, six subjects were asked to perform 9 different movements and their EMG signals were caught during the tasks from the six different forearm muscles. In order to remove the noises, the signals were filtered. Then the integral absolute average (IAV), Cepstrum coefficients and Wavelet Packet Coefficients with entropy pruning were extracted from the filtered signals as features. We used principal components analysis (PCA) for dimensionality reduction (234 to 10). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces the processing time for the pattern recognition. The proposed classifier was applied on the features and the results were led to higher than 96.7% classification rate for the 9 classes of movement. To make a comparison, support vector machine (SVM) was employed (76% classification rate for 9 classes) and the results showed a drastic supremacy of the proposed method.