Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Maryam Dorvashi; Neda Behzadfar; Ghazanfar Shahgholian
Volume 14, Issue 2 , July 2020, , Pages 109-119
Abstract
Consumption of alcohol contributes to disorders in brain. In this study, in order to detect the consumption of alcohol, electroencephalogram (EEG) signal of 20 participants (10 alcoholic and 10 control subjects) recorded by 64 channels was investigated. Frequency and non-frequency features of EEG signal ...
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Consumption of alcohol contributes to disorders in brain. In this study, in order to detect the consumption of alcohol, electroencephalogram (EEG) signal of 20 participants (10 alcoholic and 10 control subjects) recorded by 64 channels was investigated. Frequency and non-frequency features of EEG signal including power spectrum of signal, permutation entropy, approximate entropy, Katz fractal dimension and Petrosion fractal dimension were extracted to analyses the EEG signal. Statistical analysis was used to investigate the significant differences between the alcohol and control groups. The Davis-Bouldin (DB) criterion was used to select the best channel distinguishing between the alcoholic and non-alcoholic EEG signal. Results showed that between frequency features, power of lower2 alpha frequency decreased in alcoholic individuals and regarding the DB criterion, the CP3 channel (DB=1.7638) showed the best discrimination between the alcohol and control groups. Also, among the non-frequency features, the Katz fractal dimension increased in the control group and FP2 channel (DB = 0.862) had the best discrimination. Eventually, power of Lower2-alpha frequency band and Katz fractal dimension fed into the nearest neighbor classifier (KNN), 71% and 93% accuracy were achieved, respectively. According to the results, it can be concluded that the best feature and channel discriminating between alcohol and control groups is the Katz fractal dimension and FP2 channel.
Biomedical Image Processing / Medical Image Processing
Neda Behzadfar; Hamid Soltanian Zadeh
Volume 7, Issue 3 , June 2013, , Pages 219-236
Abstract
Segmentation of tumors in magnetic resonance images is an important task. However, it is quite time consuming and has low accuracy and reproducibility when performed manually. Automating the process is challenging, due to high diversity in appearance of tumor tissue in different patients and in many ...
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Segmentation of tumors in magnetic resonance images is an important task. However, it is quite time consuming and has low accuracy and reproducibility when performed manually. Automating the process is challenging, due to high diversity in appearance of tumor tissue in different patients and in many cases, similarity between tumor and normal tissues. This paper presents semi-automatic approach for analysis of multi-parametric magnetic resonance images (MRI) to segment a highly malignant brain tumor called Glioblastoma multiform (GBM). MRI studies of 12 patients with GBM tumors are used. To show that the proposed method identifies Gd-enhanced tumor pixels from T1-post contrast images minimal user interactions. They are also used to illustrate that the segmentation results obtained by the proposed approach are close to those of an expert, by showing excellent correlations among them (R2=0.97). In order to evaluate the proposed method in practical applications, effects of treatment of GBM brain tumors using Bevacizumab are predicted. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). To this end, two image series of 12 patients before and after treatment and relative changes in the volumes of the Gd-enhanced regions in T1-post contrast images are used as measure of response. The proposed method applies signal decomposition with KNN classifier to minimize user interactions and increase reproducibility of the results. Then histogram analysis is applied to extract statistical features from Gd-enhanced regions of tumor and quantify its micro structural characteristics. Predictive models developed in this work have large regression coefficients (maximum R2=0.91) indicating their capability to predict response to therapy. The results obtained by the proposed approach are compared with those of previous work where excellent correlations are obtained.
