Enhancing Arousal Level Detection in EEG Signals through Genetic Algorithm-based Feature Selection and Fast Bit Hopping.

Background: This study explores a novel approach to detecting arousal levels through the analysis of electroencephalography (EEG) signals. Leveraging the Faller database with data from 18 healthy participants, we employ a 64-channel EEG system.

Methods: The approach we employ entails the extraction of ten frequency characteristics from every channel, culminating in a feature vector of 640 dimensions for each signal instance.

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention.

Assessment of attention is of great importance as one of human cognitive abilities. Although neuropsychological tests have been developed and used to evaluate the ability to pay attention, their validity and reliability have been reduced due to some limitations such as the presence of intervention factors, including human factors, limited range of languages, and cultural influences. Therefore, direct outputs of the brain system, represented by event-related potentials (ERPs), and the analysis of its function in cognitive activities have become very important as a complementary tool to assess various types of attention.

Assessment and Classification of Mass Lesions Based on Expert Knowledge Using Mammographic Analysis.

Background: Masses are one of the most important indicators of breast cancer in mammograms, and their classification into two groups as benign and malignant is highly necessary. Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert.

Real Time Recognition of Heart Attack in a Smart Phone.

Background: In many countries, including our own, cardiovascular disease is the most common cause of mortality and morbidity. Myocardial infarction (heart attack) is of particular importance in heart disease as well as time and type of reaction to acute myocardial infarction and these can be a determining factor in patients' outcome.

Methods: In order to reduce physician attendance time and keep patients informed about their condition, the smart phone as a common communication device has been used to process data and determine patients' ECG signals.

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel.