|Institution:||California State University – Northridge|
|Department:||Department of Elec & Comp Engr|
|Keywords:||Feature extraction; Dissertations, Academic – CSUN – Engineering – Electrical and Computer Engineering.|
|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).