Abstract
Regular ECG check up is a good practice for cardiac patients as well as elderly people. In this paper we propose a low cost methodology to coarsely estimate the range of some important parameters of ECG using Photoplethysmography (PPG). PPG is easy to measure (even with a smart phone) and strongly related to human cardio-vascular system. The proposed methodology extracts a set of time domain features from PPG signal. A statistical analysis is performed to select the most relevant set of PPG features for the ECG parameters. Training model for the ECG parameters are created based on those selected features. Both artificial neural network and support vector machine based supervised learning approach is used for performance comparison. Experimental results, performed on benchmark dataset shows that good accuracy in the estimation of ECG parameters can be achieved in our proposed methodology. Results also show that the overall performance improves in using feature selection technique rather than using all the PPG features for classification.