ECG signal compression by predictive coding and Set Partitioning in Hierarchical Trees (SPIHT)

Grafika Jati, Aprinaldi, Sani M. Isa, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper we present a method for multi-lead ECG signal compression using Predictive Coding combined with Set Partitioning In Hierarchical Trees (SPIHT). We utilize linear prediction between the beats to exploit the high correlation among those beats. This method can optimize the redundancy between adjacent samples and adjacent beats. Predictive coding is the next step after beat reordering step. The purpose of using predictive coding is to minimize amplitude variance of 2D ECG array so the compression error can be minimize. The experiments from selected records from MIT-BIH arrhythmia database shows that the proposed method is more efficient for ECG signal compression compared with original SPIHT and relatively have lower distortion with the same compression ratios compared to the other wavelet transformation techniques.

Original languageEnglish
Title of host publicationICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages257-262
Number of pages6
ISBN (Electronic)9781509003624
DOIs
Publication statusPublished - 19 Feb 2016
EventInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015 - Depok, Indonesia
Duration: 10 Oct 201511 Oct 2015

Publication series

NameICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

ConferenceInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015
CountryIndonesia
CityDepok
Period10/10/1511/10/15

Keywords

  • Compression
  • Electrocardiogram
  • Predictive Coding
  • SPIHT
  • wavelet transform

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