A computationally light-weight real-time classification method to identify different ECG signals

Chin, F, Fang, Q and Cosic, I 2011, 'A computationally light-weight real-time classification method to identify different ECG signals', in Ming Xuan (ed.) Proceedings of 2011 International Symposium on Bioelectronics and Bioinformatics, ISBB 2011, Suzhou, China, 3-5 November, 2011, pp. 287-290.


Document type: Conference Paper
Collection: Conference Papers

Title A computationally light-weight real-time classification method to identify different ECG signals
Author(s) Chin, F
Fang, Q
Cosic, I
Year 2011
Conference name 2011 International Symposium onBioelectronics and Bioinformatics (ISBB)
Conference location Suzhou, China
Conference dates 3-5 November, 2011
Proceedings title Proceedings of 2011 International Symposium on Bioelectronics and Bioinformatics, ISBB 2011
Editor(s) Ming Xuan
Publisher IEEE
Place of publication United States
Start page 287
End page 290
Total pages 4
Abstract Ventricular arrhythmia is the main cause of cardiac arrest in patients with chronic heart disease. An undetected episode of ventricular tachycardia (VT) can be fatal if emergency medical assistance is not provided. Therefore, it is important to devise a real-time mobile ECG signal analysis algorithm for detection of ventricular tachycardia (VT). This paper presents an algorithm for automatic identification of normal sinus rhythm (NSR) and ventricular tachycardia (VT) which is applicable in a mobile environment. The algorithm employs peak-valley detector and cross-correlation technique to compute a feature vector. The selected features are beats-per-minute (BPM), NSR template accuracy and VT template accuracy. Based on the selected features, a fuzzy k-NN classifier is trained for classification. The algorithm specificity and sensitivity for classifying between NSR and VT ECG signal is 92.5% and 93.5% respectively.
Subjects Networking and Communications
Keyword(s) Analysis algorithms
Automatic identification
Cardiac arrest
Classification methods
Cross correlation techniques
ECG signals
Feature vectors
Fuzzy k-NN
Heart disease
Light weight
Mobile environments
Normal sinus rhythm
Ventricular arrhythmias
Ventricular tachycardia
DOI - identifier 10.1109/ISBB.2011.6107703
Copyright notice © 2011 IEEE
ISBN 9781457700774
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