Exploring recurrence quantification analysis and fractal dimension algorithms for diagnosis of encephalopathy.

Electroencephalography (EEG) is a crucial non-invasive medical tool for diagnosing neurological disorder called encephalopathy. There is a requirement for powerful signal processing algorithms as EEG patterns in encephalopathies are not specific to a particular etiology. As visual examination and linear methods of EEG analysis are not sufficient to get the subtle information regarding various neuro pathologies, non-linear analysis methods can be employed for exploring the dynamic, complex and chaotic nature of EEG signals. This work aims identifying and differentiating the patterns specific to cerebral dysfunctions associated with Encephalopathy using Recurrence Quantification Analysis and Fractal Dimension algorithms. This study analysed six RQA features, namely, recurrence rate, determinism, laminarity, diagonal length, diagonal entropy and trapping time and comparing them with fractal dimensions, namely, Higuchi's and Katz's fractal dimension. Fractal dimensions were found to be lower for encephalopathy cases showing decreased complexity when compared to that of normal healthy subjects. On the other hand, RQA features were found to be higher for encephalopathy cases indicating higher recurrence and more periodic patterns in EEGs of encephalopathy compared to that of normal healthy controls. The feature reduction was then performed using Principal Component Analysis and fed to three promising classifiers: SVM, Random Forest and Multi-layer Perceptron. The resultant system provides a practically realizable pipeline for the diagnosis of encephalopathy.

Metal-decorated γ-graphyne as a drug transporting agent for the mercaptopurine chemotherapy drug: a DFT study.

In recent years, carbon-based two-dimensional (2D) materials have gained popularity as the carriers of various anticancer therapy drugs, which could reduce the crucial side effects by directly applying the drugs to the intended tumor cells. In this study, through first-principles density functional theory simulations, we have investigated the adsorption properties of a famous cancer chemotherapy drug called mercaptopurine (MC) on a 2D γ-graphyne (GYN) monolayer. Analyzing the geometric and electronic properties, we can summarize that the MC interaction with the pristine GYN is weak, with a small adsorption energy of -0.15 eV, which is too low for potential applications. Therefore, we have decorated the GYN monolayer with biocompatible metals such as Al, Ag, and Cu to trigger the adsorption capacity. The Al- and Cu-decorated GYN offered improved adsorption towards MC compared to the pristine case. The drug release from these metal-decorated systems was examined by creating an acidic environment. In addition, the desorption temperature of the drug from the system was also evaluated using ab initio molecular dynamics simulations. The calculations demonstrated that the Al-decorated GYN is a potential vehicle for MC drug delivery because of the favourable adsorption energy of -0.63 eV, charge transfer of 0.17e and desorption temperature above 270 K. The current research will stimulate the investigation of other low-dimensional carbon materials for drug-delivery applications.

Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy.

Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entropies and chaotic features. This study aims at examining the discriminative power of individual features and their combination in the diagnosis of a neuro-pathological condition called encephalopathy. Feature combination is accomplished with the help of feature selection using Gini impurity score that improves discriminative power and keeps redundancy minimal. Further, three widely used non-parametric classifiers which are known to be effective with wavelet features on EEG signals - Support Vector Machine, Random Forest, Multilayer Perceptron - are employed for disease classification. The models created by the combination of aforementioned stages are analysed and evaluated with performance scores, leading to an optimal model for automated diagnostic applications.