TY - GEN
T1 - ECG classification and analysis in a zigbee wireless sensor network
AU - Barrett, Enda
AU - Chambers, Des
AU - Rotariu, Cosmin
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Ambulatory ECG monitoring
KW - Arrhythmia analysis
KW - Biomedical Signal Processing
KW - Wireless Sensor Networks
UR - https://www.scopus.com/pages/publications/67650535212
M3 - Conference Publication
SN - 9789898111654
T3 - BIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing
SP - 322
EP - 326
BT - BIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing
T2 - 2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2009
Y2 - 14 January 2009 through 17 January 2009
ER -