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Evaluation of electrocardiogram beat detection algorithms: patient specific versus generic training

Last, T, Nugent, CD and Owens, FJ (2007) Evaluation of electrocardiogram beat detection algorithms: patient specific versus generic training. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France. UNSPECIFIED. 4 pp. [Conference contribution]

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DOI: 10.1109/IEMBS.2007.4353012


The present study discusses two different training techniques for electrocardiogram (ECG) beat detection algorithms. The first technique is a patient specific training method which uses data from the patient's ECG signal to train the beat detector. The second technique is more generic as opposed to patient specific and uses ECG information from a database consisting of a number of patient records to train the detector. Four different beat detection algorithms were considered to facilitate the evaluation of the influence of the training techniques in relation to beat detection performance; a non-syntactic approach, a cross-correlation (CC) approach, a multi-component based CC technique and a multi-component based neural network (NN) technique. An ECG database containing approximately 3000 annotated beats was used for training and test. Superior results were attained with the patient specific training technique. The performance of the two multi-component based classifiers were increased by up to 22% for P-wave and T-wave detection for the patient specific training approach compared to the generic training approach.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Smart Environments
ID Code:7034
Deposited By: Professor Christopher Nugent
Deposited On:29 Jan 2010 14:12
Last Modified:18 Aug 2011 10:56

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