AbstractsComputer Science

QRS classification using support vector machine

by Triti Kadkhodayan

Institution: California State University – Northridge
Department: Department of Elec & Comp Engr
Degree: MS
Year: 2015
Keywords: Feature extraction; Dissertations, Academic  – CSUN  – Engineering  – Electrical and Computer Engineering.
Record ID: 2060866
Full text PDF: http://hdl.handle.net/10211.3/133281


An electrocardiogram (ECG or EKG) is a test that shows the nature of heart conditions by measuring electrical activity of the heart. This has been used as all effective method to identify any heart malfunction prior to a cardiac arrest. Machine learning techniques are a good tool for diagnosis since they are able to observe things that are not seen by naked eyes. Support vector machine (SVM) is one of the most effective technique for learning from the provided data. Its classification is vital to saving life. In this project the records were selected from the open source MIT-BIH arrhythmia and the data is divided into training and testing sets. Norma and abnormal heartbeats have been properly classified such as left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC).