ECGML- electrocardiography using machine learning

World Congress on Hypertension and Cardiovascular Diseases

November 21-22, 2018 | Paris, France

Wala Awad,Anas AbuZaitoun

An-Najah National University,
Palestine

Scientific Tracks Abstracts: J Cardiovasc Dis Diagn

Abstract :

Electrocardiography has been used extensively in diagnoses in almost all healthcare facilities. Upgrading this tool will reform diagnosis, and is expected to improve diagnosis and patient care. Thus, this project was designed to maximize potential benefits gained when machine learning technology is incorporated into ECG analysis. ECGML- electrocardiography using machine learning is a project created to enhance the performance of the typical ECG scanner by widening the area of its results and improving its accuracy. Using this technology, ECG can be used to not only show basic information about the heart but also to help diagnosing more than fifteen other arrhythmias precisely. Machine learning and Googleâ??s Tensorflow were used to create a program that - when trained enoughwill be able to diagnose those arrhythmias in the most accurate way possible. It is an easier and a faster way to be used in this field rather than the typical way.

Biography :

Anas AbuZaitoun and Wala Awad are both computer engineering students at An-Najah National University. They will be graduating in less than 2 years, and they have a great interest in new technologies and machine learning in particular. Anas AbuZaitoun is not the typical type of students, he has been the organizer of GDG – Nablus branch for two years so far, and he is an active IEEE member. He is an Android developer, and he worked on many projects before, including mobile apps and desktop apps. He also attended Google’s I/O festival in San Jose this year. Wala Awad is an active member in both GDG and IEEE, and she is an Android developer as well. She is willing to get her master’s degree on machine learning as soon as she graduates. They have been participating in technical competitions that will employ both hardware and software.

E-mail: anasomarzaitoun@gmail.com