Energy-Based DCT Approach for PPG Compression.

Abstract

With the rapid development of communications, information technology, and Internet of Things (IoT), photoplethysmography (PPG) has achieved prevalence in telemedicine and remote health monitoring using wearable devices. As these devices are resource-constrained, efficient compression techniques are necessary for optimal storage and power consumption management with sustained clinical morphology of PPG signal. This work presents a new approach for PPG compression based on the energy of discrete cosine transform (DCT). With better data compression, a significant reduction of noise is also obtained in the reconstructed signal. The performance evaluation is done on multiple publicly available databases, an average compression ratio of 37.46 is achieved on MIMIC-II database with percentage root-mean-square difference of 0.0688, which is significantly higher than the existing PPG-compression approaches in the literature. Clinical relevance- The work is intended to promote the integration of this combined compression and denoising algorithm for PPG-based devices. The approach is helpful to impart optimization of memory and preserve the clinically relevant information of PPG signals in terms of its useful fiducial points. Further it can amplify ubiquitous computing health monitoring with faster diagnosis and feedback.