A video-based eye pupil detection system for diagnosing bipolar disorder

WOS: 000326514200019Eye pupil detection systems have become increasingly popular in image processing and computer vision applications in medical systems. In this study, a video-based eye pupil detection system is developed for diagnosing bipolar disorder. Bipolar disorder is a condition in which people experience changes in cognitive processes and abilities, including reduced attentional and executive capabilities and impaired memory. In order to detect these abnormal behaviors, a number of neuropsychological tests are also designed to measure attentional and executive abilities. The system acquires the position and radius information of eye pupils in video sequences using an active contour snake model with an ellipse-fitting algorithm. The system also determines the time duration of the eye pupils looking at certain regions and the duration of making decisions during the neuropsychological tests. The tests are applied to 2 different groups consisting of people with bipolar disorder (bipolar group) and people without bipolar disorder (control group) in order to mathematically model the people with bipolar disorder. The mathematical modeling is performed by using the support vector machines method. It is a supervised learning method that analyzes data and recognizes patterns for classification. The developed system acquires data from the being tested and it classifies the person as bipolar or nonbipolar based on the learned mathematical model

How Generous is the ELLPACK Sparse Matrix Storage Scheme for Finite Element Computations

Sparse matrices are occasionally encountered during solutions of various problems by means of numerical methods, such as the finite element method. ELLPACK sparse matrix storage scheme, one of the most widely used methods due to its implementation ease, is investigated in this study. The scheme uses excessive memory due to its definition. For the conventional finite element method, where the node elements are used, the excessive memory caused by redundant entries in the ELLPACK sparse matrix storage scheme becomes negligible for large scale problems. On the other hand, our analyses show that the redundancy is still considerable for the occasions where facet or edge elements have to be used