Denoising and Compression of ECG for Memory Optimisation in Mobile Devices

Abstract

Owing to the growing demand for mobile healthcare and fitness monitoring devices, there is a need of optimization of memory space keeping in mind the device size constraints. Monitoring of various organs in a person has been developed for which the person has to go to a hospital or a diagnostic centre. And the monitoring of the heart has become very crucial as the death rates due to cardiac arrests have increased with no age barriers. Mobile ECG devices are growing fast due to the increasing health consciousness in every person and organizations.

The smaller devices to wear, the easier they are to carry. Mobile ECG sensors are prone to a lot of noise. This paper discusses on denoising technique using wavelet transform. The size constraints have pushed researchers to develop codes for memory optimization, to limit the capacity of Memory devices, and thus reducing the size to a certain extent. In this paper, set partitioning in hierarchical trees (SPIHT) coding algorithm is used for compression. SPIHT has achieved a lot of prominent success in compression of images. SPIHT for one dimensional signal is used. Wavelet transform along with SPIHT coding algorithm and encryption of the compressed data is discussed using a couple pre recorded ECG signals generated in MATLAB.