Data Compression of Photoplethysmogram Signal for IoT Application

Abstract

Cardiovascular diseases (CVD) or abnormalities are the leading causes of mortality in today’s era throughout the world. A human life can be saved on proper time, if they are diagnosed with diseases at the earliest. The major popular modalities in monitoring the electrical activity of the heart using the Electrocardiogram (ECG), the Phonocardiogram (PCG), and the Photoplethysmogram (PPG). Electrocardiogram (ECG) readings with sophisticated signal processing tools hinders because of non-portability. Whereas, PPG has become increasingly common due to its low cost, wireless capabilities, and relatively small size and optical nature. Including normal patient, Cardiac dis-order such as hypertension, ischemic heart disease, myocardial infarction, among others, disrupts the body’s volumetric blood flow rate. The work proposes a revolutionary strategy to utilize the IoT-based platform to download and access the vast amount of bio signals data effectively using data-compression technique to save time and cost without compromising accuracy. The solution is the implementation of Gaussian and Fourier models of the PPG datasets and, after that, compression of each model parameter. The enormous data set of various cardiac patients contains some vital information for which the implementation of such a robust data compression tool or data compression mechanism is necessary. So, the information can be accessed from anywhere in the world with a high-speed internet connection and with the specific settings for (Unique Patient ID) for PPG monitoring.