Publication

Abstract : In this work we address the data privacy concerns and the need of extensive data sets by the deep learning models. We account the need of low computational frame work along with flexible architecture and diverse requirements. We use a novel approach to generate extensive synthetic P wave morphology sets from fifteen cycles of the real ECG record (MIT-BIH Arrhythmia dataset). Our approach is based on development of Python framework of algorithms. It consists of a composition of a variability trend of discrete realistic ECG record samples. We used regression modalities (polynomial fit and spline fit) in a novel way in