Compressive Sensing Algorithm With Adaptive Best Mother Wavelet For Energy Efficient PPG Signal Compression In WBAN Node

Abstract

Wireless Body Area Network (WBAN) is a collection of wireless biosensors worn on the body, in which each sensor node is capable of computing and communicating with other nodes or devices like smart phones, Personal Digital Assistant (PDA), hand held devices etc,. The wearable nodes are powered by battery and need to be always functional for continuous remote monitoring of patients which demands that node life time has to be prolonged to the maximum extent. One of the best solutions for this issue is to go for data compression at the node. In this context, Compressive Sensing (CS) based energy efficient  min PRD based Adaptive Best Mother Wavelet (ABMW) choise model has been suggested and tested for compression of Photoplethesmography (PPG) data. Test data have been acquired from one of the normal subject using Arduino Uno R3. Validation of the algorithm has been carried out by applying on MIMIC-II database and acquired signal. It is found that proposed model maintenances right demonstration of the physiological actions and quicker reference to  its pioneers because recovery of 10 seconds PPG data from MIMIC-II database it  has taken average execution time = 1.103 sec for compression and  simulation results indicate that proposed model attained avg PRD=4.78, SNR =26.71 and CR = 63.29. The obtained PRD value is with in the 2-9% of  the preferred set for medical suitability.