Hardware Implementation Of ECG Signal Compression Using SPIHT. Issue 1 (1st August 2022)

• Record Type: Journal Article
• Authors: Shanmuga sundari, M; Loganathan, S; Sujatha, C.M
• Is Part Of: Journal of physics. Volume 2318:Issue 1(2022)
• Publication Date: 2022-08-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2318/1/012017
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Cardiovascular disorder is a primary cause of mortality throughout the world in both developed and underdeveloped countries. Continuous cardiac monitoring enables clinicians to identify arrhythmias and other heart conditions. Tele-cardiology introduces remote monitoring devices for tracking the cardiac activity of the patients. The large volume of Electrocardiogram (ECG) data needs to be stored, processed and transmitted by these portable health care devices. The implementation of ECG compression in hardware platform is crucial for continuous health monitoring applications. The aim of this work is to implement field programmable gate array based set partitioning in hierarchical trees-based electrocardiogram compression. Discrete wavelet transform method is employed to break up the signal into sub bands. The transformed coefficients after discrete wavelet transform are passed through dead zone quantization which rejects low magnitude values of transformed coefficients lying around zero. These quantized coefficients are then encoded by lossless set partitioning used in hierarchical trees compression approach. The introduction of dead zone quantization in the proposed technique is found to be effective and yields an increased compression ratio of 10.33 with decreased distortion value of 1.04 percent for ECG record 117 of MIT-BIH arrhythmia database.