Smart phone based blood pressure indicator

In this paper, we propose a methodology to estimate the range of human blood pressure (BP) using Photoplethysmography (PPG). 12 time domain features and 7 frequency domain features are pointed out and extracted from the PPG signal. A feature selection algorithm based on Maximal Information Coefficient (MIC) is presented to reduce the dimensionality of the feature set to effective ones, thereby cutting down resource requirements. Support Vector Machine (SVM) is used to classify the BP values into separate bins. The proposed methodology is validated and tested on a standard benchmark clean dataset as well as phone captured noisy dataset to justify its robustness and efficiency. Apart from a commending performance improvement, BP estimation is achieved with minimal features and processing, making the algorithm light weight for porting on smart phones.