Cardiovascular diseases (CVD) are a leading cause of unnecessary hospital admissions as well as fatalities placing an immense burden on the healthcare industry. A process to provide timely intervention can reduce the morbidity rate as well as control rising costs. Patients with cardiovascular diseases require quick intervention. Towards that end, automated detection of abnormal heartbeats captured by electronic cardiogram (ECG) signals is vital. While cardiologists can identify different heartbeat morphologies quite accurately among different patients, the manual evaluation is tedious and time consuming. In this chapter, we propose new features from the time and frequency domains and furthermore, feature normalization techniques to reduce inter-patient and intra-patient variations in heartbeat cycles. Our results using the adaptive learning based classifier emulate those reported in existing literature and in most cases deliver improved performance, while eliminating the need for labeling of signals by domain experts. © 2015 - IOS Press and the authors.