ABSTRACT
Manual identication of ECG heart-beat classes by cardiologists is time consuming and cumbersome. These professionals rely on computer based methods for determination of these heart-disease types. The methods are organized into two major categories as either raw time series-based approaches or feature-based approaches. In this work, existing literature is organized into a proposed taxonomy based on dichotomies involving full time series-based versus feature-based and AAMI versus Non-AAMI based distinctions. The basic contributions of this work are systematic review of literature on heart-beat abnormality detection, identifying gaps in the literature to propose novel approaches for addressing the gaps.
