A Novel Technique to Compress Photoplethysmogram Signal: Improvised with Particle Swarm Optimization and Rivest-Shamir-Adleman Algorithm

Photoelectric Photoplethysmography is a non-invasive method used to measure the volumetric variations in blood circulation due to cardiac, respiratory and other physiological activities in the body. Photoplethysmogram (PPG) signals can therefore be used to monitor vital cardiovascular parameters such as Heart Rate (HR), Systolic Peak, Diastolic Peak, Dicrotic Notch, etc. Therefore, it is imperative, such signal be compressed so that it can be stored and transmitted keeping the vital physiological parameters intact. In this paper we propose an efficient compression and encryption algorithm based on Particle Swarm Optimization (PSO) and Rivest-Shamir-Adleman (RSA) algorithm as a novel approach to compress PPG signal whilst keeping the morphology and information content of the signal intact. To analyze the performance of our proposed algorithm, we used PPG data from BIDMC database along with volunteers' data. Several metrics such as Pearson Correlation Coefficient (PCC), Mean Squared Error (MSE), and Percentage Root Difference (PRD) are used to assess the quality of the signal before and after compression. Our proposed method achieved a Compression Ratio (CR) of 82.46, PRD of 2.19 for BIDMC and a CR of 88.48 with PRD of 2.67 for volunteer's data along with a PCC of 0.99 and almost negligible MSE for both datasets.