Compression of ECG Signals Using an Adaptive Algorithm Based on DWT and SPIHT Decoder for Health Care Applications

A novel concept of mobile health applications has been proposed as a combination of telecommunication and multimedia technologies in providing telemedicine facility. In medical practises, the storage capacity of ECG records is a major issue and data could take large space for storage to save the records. To deal this issue, compression of ECG signal is widely used along with adaptive algorithm for noise removal of the signal. In this paper, an efficient method of ECG signal compression using discrete wavelet transform and SPIHT Decoder is proposed. The Adaptive Filtering techniques are mostly used to resolve the problems in digital communications environment and in the area of biomedical engineering. Various adaptive filtering methods have been proposed for cancellation of spurious noise in Electrocardiogram (ECG) signals. In order to make compression of ECG signals in which mainly need to remove the noise interference, a new adaptive filtering technique using FNLMS algorithm was proposed to reduce mean square error to denoise the ECG signal. A novel and powerful ECG signal compression using Discrete Wavelet Transform based image compression technique called Set Partitioning in Hierarchical Trees (SPIHT) is becoming a state-of-art algorithm for image compression due to high image quality and simple quantization algorithm. The proposed design was implemented in Xilinx platform and MATLAB tool. The performance of SPIHT Decoder design using FNLMS algorithm can be estimated by the metric parameters like Compression Ratio, Signal to Noise Ratio, Percent Root Mean Square Difference and Root Mean Square Error.