Clinical quality guaranteed physiological data compression in mobile health monitoring

Abstract

Data compression is essential for continuously collected physiological signals in mobile health monitoring applications in order to prolong battery lifetime and reduce transmission costs. Transformation-based compression techniques have been widely used due to their high compression ratio; however, distortion caused during compression process degrades clinical quality of decompressed signals. In this paper, we propose a simple method called "Critical Markers" method that is based on detection of peaks and valleys in the original signal. When used in conjunction with existing transformation-based compression methods, the critical markers corrects the distortions without compromising the fidelity of the compressed output. The critical markers can also be used standalone to replace existing compression methods in certain types of diagnosis, thus reducing line and processor overhead. We have implemented the proposed method on a smartphone and have tested it with real ECG and PPG data sets. The experimental results confirm that our method maintains high compression performance while also guaranteeing high clinical quality.