مقالات رزومه دکتر فاطمه جمشیدی

Schizophrenia is a disorder of the brain in which people misinterpret reality. This mental disorder is characterized by behavioral symptoms such as hallucinations and speech disorders. Electroencephalogram (EEG) signal indicates brain disorders and is widely used to study brain diseases. The aim of this article is to automatically detect schizophrenia based on the EEG signal. A common method in research is the manual extraction of features from the EEG signal. Because deep learning algorithms have the ability to automatically extract important features and classify them, in this study, in order to extract more useful features, the EEG signal was applied to an eleven-layer convolutional deep recursive neural network. The EEG signals collected at the Warsaw Institute from 14 healthy individuals and 14 patients with schizophrenia are studied here. The mean values of the accuracy evaluation criteria of the model including Accuracy, Sensitivity, Specificity and PPV for the proposed model were 98.79%, 98.73%, 98.86% and 99.06%, respectively, which improved the performance of the proposed model for classifying schizophrenic patients and approves healthy people compared to previous models. The proposed model can be used as a diagnostic tool to help physicians diagnose the early stages of schizophrenia.

Any heart activity disorder may lead an irregularity in is rhythm, or cardiac arrhythmia. An ECG signal is one of the major tools for classifying different types of cardiac arrhythmias. ECG signals usually contain various noises. To have a better signal processing, it is essential to remove noises in a way that a signal structure never become subject to distortion. After the step of noise removal, selection of an appropriate method is of paramount importance for feature extraction. Optimal features can be selected to improve efficiency and reduce calculations.

This article used the ensemble empirical mode decomposition (EEMD) in which any intrinsic mode function (IMF) contains only a single frequency component for noise removal. The noise removal operation with the least distortion is possible using an appropriate windowing on a QRS complex containing sum of the first three IMFs. Later, the remaining noises can be removed using discrete wavelet transform (DWT). The results of using the EEMD-DWT combined method were compared with EMD and DWT combination. After the noise removal step, feature extraction was performed through a wavelet packet decomposition. It is capable of signal decomposition at all frequencies. Multiple objective binary particle swarm optimization (MOBPSO) method was used to select optimal features and the effect of this method on the results was examined. Finally, the back propagation neural network (BPNN) and a support vector machine based on particle swarm optimization were used for classification.

This article used 17 signals received from the MIT-BIH database. The acquired data belong to 6 different types of classes. After pre-processing, feature extraction, feature selection, and classification on the input data, it is observed that the proposed technique of EEMD-DWT is an appropriate method for noise removal and MOBPSO is a suitable method for the selection of best features. The BPNN classifier managed to classify cardiac arrhythmias with a higher accuracy and the values for accuracy, sensitivity, specificity, and positive predictive value were 99.12%, 97.08%, 99.38%, and 97.12%, respectively.