Diaz, JD, Escalona, OJ, Castro, NC, Anderson, JMCC, Glover, B and Manoharan, G (2010) Predicting Transthoracic Defibrillation Shocks Outcome in the Cardioversion of Atrial Fibrillation Employing Support Vector Machines. In: Computing in Cardiology, Belfast-UK. IEEE. Vol 37 4 pp. [Conference contribution]
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In this work, we use support vector machines (SVM) to predict if a defibrillation shock is likely to be successful or not in the cardioversion of persistent AF patients. TheECG signals of 47 patients elected for electrical cardioversion treatment were collected at the Royal Victoria Hospital in Belfast city, NI-UK. Signal processing was performed on ECG segments prior each shock. Three electrocardiographic indexes were extracted and used as input: the dominant atrial fibrillatory frequency, the mean and the standard deviation of the R-R interval time series of the ECG segments. We trained SVM using about 40% of the data. SVM could predict the outcome of 89% of low-energy shocks below or = 100 [J], with a sensitivity (SE) of 87.50% and specificity (SP) of 98.8%. As a remarkable result, theoutcome of higher energy shocks (above or = 150 [J]) could be predicted with 100% exactitude.
|Item Type:||Conference contribution (Paper)|
|Keywords:||AF Cardioversion Atrial Fibrillation AF Defibrillation Support Vector Machine Arrhythmia Treatment ECG Time Series QRS Cancellation|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Engineering
|Research Institutes and Groups:||Engineering Research Institute|
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
|Deposited By:||Professor Omar Escalona|
|Deposited On:||09 May 2012 13:24|
|Last Modified:||09 May 2012 13:24|
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