ECG classification and analysis in a zigbee wireless sensor network

Research output: Chapter in Book or Conference Publication/ProceedingConference Publicationpeer-review

2 Citations (Scopus)

Abstract

Wireless technology has become ubiquitous in our daily lives. From 802.11 to Bluetooth we have become familiar with new technologies and expectations are rife as to its potential. The medical world is potentially lucrative for the use of such technology. The ability to improve patient comfort, monitor patients remotely and increase device mobility should all contribute handsomely to patient life quality. It also offers the unique opportunity to monitor ambulatory patients in a real-time environment. Outlined is an approach to integrate an Electrocardiogram (ECG) classifier into an overall wireless patient monitoring system enabling real-time classification and analysis of ECG data. Our research has shown that it is possible to use the open source classifier (Hamilton, 2002) in a wireless sensor network for beat detection and arrhythmia classification. We have tested the classifier with up to 80 simulated sensors proving that its lightweight implementation enables it to cope perfectly with only minor modifications needed. It was found that the addition of multiples of sensors produced on average 0.01% performance degradation.

Original languageEnglish
Title of host publicationBIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing
Pages322-326
Number of pages5
Publication statusPublished - 2009
Event2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009 - Porto, Portugal
Duration: 14 Jan 200917 Jan 2009

Publication series

NameBIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing

Conference

Conference2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009
Country/TerritoryPortugal
CityPorto
Period14/01/0917/01/09

Keywords

  • Ambulatory ECG monitoring
  • Arrhythmia analysis
  • Biomedical Signal Processing
  • Wireless Sensor Networks

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