ECG Compression Techniques of Spiht Decoder for Mobile Health Applications

Abstract

The demand for remote health applications is increasing as e-health monitoring devices improve. For remote health applications, e-health monitoring devices are becoming more and more popular. Cardiovascular diseases (CVDs) were recently thought to be the most common chronic illnesses among patients. Therefore, it is necessary to have more diagnosis and medical therapy and also requires transformation of information from cardiac patients to distant hospitals. There are some challenges according to the devices to take over like excess energy consumption, excessive power consumption, data protection, storage space as well as transmission rate. In addition to that, the Real-time processing of electrocardiogram (ECG) data is required, and the captured data must have high accuracy and lossless compression. A SPIHT decoder with enhanced capabilities of Set Partitioning in Hierarchical Tree has been designed for signal processing. The transmission of the ECG signal on a wireless channel is seriously impacted by numerous disturbances because of the inadequate channel in medical applications. Input database can be taken using the database named as MIT-BIH and was developed on Xilinx platform and MATLAB. SPIHT decoder results can be estimated by different algorithms and also measured the parameters like to demonstrate its effectiveness, it is compared to existing designs and measured for the Compression Ratio (CR), Signal to Noise Ratio (SNR), Percentage Root Mean Square Difference (PRD), and Root Mean Square Error (RMSE). Many ECG recordings from the Arrhythmia Database are used in the tests for this lossy compression/decompression technique. The collected results show how the suggested method can increase compression ratio while preserving high signal quality.