ECG Compression Using Dynamic Tree Vector Quantization in Wavelet Domain

Abstract

In this paper, we propose a novel vector quantizer (VQ) in the wavelet domain for the compression of electrocardiogram (ECG) signals. A vector called tree vector is formed first in a novel structure, where wavelet transformed (WT) coefficients in the vector are arranged in the order of a hierarchical tree. Then, the tree vectors extracted from various WT subbands are collected in one single codebook. Finally, a distortion-constrained codebook replenishment mechanism is incorporated into the VQ, where codevectors can be updated dynamically, to guarantee reliable quality of reconstructed ECG waveforms. With the proposed approach both visual quality and the objective quality in terms of the percent of root-mean-square difference (PRD) are excellent even in a very low bit rate. For the entire 48 records of Lead ii ECG data in the MIT/BIH database, an average PRD of 7.3 % at 146 bits/s is obtained. For the same test data under consideration, the proposed method outperforms many recently published ones, including the best one known as the SPIHT (set partitioning in hierarchical trees). Keywords - wavelet transform, vector quantization, tree vector, distortion-constrained codebook replenishment

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409684

Entities

People

  • Heng-lin Yen
  • Shaou-gang Miaou

Organizations

  • Chung Yuan Christian University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Coders
  • Coding
  • Coefficients
  • Compression
  • Data Compression
  • Data Rate
  • Data Sets
  • Databases
  • Decoding
  • Distortion
  • Engineering
  • Image Compression
  • Military Research
  • Signal Processing
  • Simulations
  • Wavelet Transforms

Fields of Study

  • Engineering

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.