Remote healthcare monitoring for cardiovascular diseases (CVDs) is of paramount importance throughout the world because of their high mortality rate. Therefore, in the research community, a significant amount of thrust is given to identification and prevention of CVDs. Supporting the research, ECG feature extraction and ECG signal classification algorithms' were developed as an effort to automate the diagnosis process of CVDs remotely. These algorithms' inherent assumption is the availability of one complete ECG frame having P-wave, QRS complex, T-wave and its corresponding feature points P, Q, R, S, T. Such complete ECG frames are supplied to the feature extraction and classification algorithms by human intervention. In this paper, we propose an on chip real time automated ECG frame identification methodology for obtaining the complete ECG frames in an automated fashion by identifying the start and end points of ECG frames. The proposed methodology has been implemented using Discrete wavelet transform (DWT) in a low complexity architectural implementation by resource sharing. This entails scope in completely automating the CVD prognosis suitable for power constrained environment ubiquitously. The proposed methodology was tested on 108 patients data over PTBDB, CSE DB and in house IITH DB and obtained Percentage Errors (PE)s% are 1.11, 0.52 and 1.85 respectively. The PE (%) is calculated by subtracting the obtained results to that annotated values provided by Cardiologists which are taken as the golden standard for each of the mentioned databases. © 2014 IEEE.