Mohamed Ezzeldin A. Bashir
Affiliation: Faculty of Computer Science
University of Medical Sciences and Technology.
Title: Self-motivated Learning Grounds Competence Real-Time Cardiac Arrhythmia Detection
ECG signals are a very important medical instrument that can be utilized by clinicians to extract very useful information about the functional status of the heart. There has been a great deal of interest in systems that provide real time ECG classification through an intermediary local computer between the sensor and control center.It is vital for the automated system to accurately detect and classify ECG signals very fast to provide a useful means for tracing the heart?s health in the right time. The effectiveness of such systems is affected by several factors, including the ECG signals, estimated ECG?s features and descriptors, the dataset used for learning purpose, and the classification model applied. This talk is concerned with the challenges for training the classifiers model with updated data to facilitate the process of developing real time cardiac health monitoring systems in detail, the current status of the self-motivated training techniques will be reviewed, focusing on trigger learning, active learning, and associative learning methods. Moreover, performance comparisons of these techniques will be presented.
Mohamed Ezzeldin A. Bashir received the M.S. degree in computer science and information technology fromUniversity of Gezira, Sudan in 2003 and the Ph.D. degree in Computer Science from Chungbuk National University, Cheongju, Korea, in 2012. He is assistant professor and dean faculty of computer science and information technology, University of Medical Sciences and Technology, Khartoum State, Sudan.
His research interests include data mining, information retrieval, database systems, bioinformatics, and biomedicine.