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Purpose: Parkinson's Disease (PD) is a neuro-degenerative interminable issue causing dynamic loss of dopamine-creating synapses, which is one of the most far reaching ailments after Alzheimer's infection. In this paper, a system for the classification of Parkinson’s disease tremor using noninvasive measurement and frequency domain features is represented. Materials and Methods: Tremor time-series of Parkinson's disease patients were recorded via a smartphone’s accelerometer sensor. Short-Time Fourier Transform (STFT) was applied to transform the time-domain signal into the frequency domain with high time-frequency resolution. Several frequency features, including mean, max of power spectral density and side frequency have been extracted and by using the FDR algorithm combinations of features carried enough information to reliably assess the severity of tremor in Parkinson patients were determined. Results: Four different classifiers were implemented to estimate the severity of tremors based on the Unified Parkinson's Disease Rating Scale (UPDRS) in Parkinson's disease patients. Conclusion: Classifiers’ estimation was compared to clinical scores derived via neurologist UPDRS annotation on Parkinson's disease patients’ tremor. The best accuracy achieved was 95.91±1.51.

Color Vision Deficiency (CVD) is one of the most common types of vision deficiency. People with CVD have difficulty seeing color spectra depending on what types of retina photoreceptors are impaired. In this paper, the Ishihara test with 38 plates was used to examine the Electroencephalogram (EEG) of ten subjects with CVD plus ten healthy individuals. The recording was performed according to the 10–20 international system. The C-based software was programmed so that subjects could select the number or path in each test plate in the software options while recording EEG. Frequency features in different frequency bands were extracted from the EEG signals of the two groups during the Ishihara test. Statistically significant differences (P < 0.05) between features were assessed by independent samples t-test with False Discovery Rate (FDR) correction. Also, the K-nearest neighbor classifier (KNN) was used to classify the two groups. The results revealed that the most significant difference between the two groups in the Ishihara test images occurred for the electrodes located in the right temporoparietal areas (P4 and T6) of the brain in the Delta, Theta, Beta1, and Beta2 frequency bands. The KNN classifier, using the signals that reported the greatest statistical difference between the two groups, showed that the two groups were distinguishable with 85.2% accuracy. In this way, images from the Ishihara test that would provide the most accurate classification were identified. In conclusion, this research provided new insights into EEG signals of subjects with CVD and healthy subjects based on the Ishihara color vision test.

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Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.