Detection of Premature Ventricular Contraction Beats Using ANN
نویسندگان
چکیده
Detection and classification of ventricular complexities from the electrocardiogram (ECG) is of considerable importance in critical care and patient monitoring for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in relation to life-threatening arrhythmias. Model based approach for detection of PVC is a common one. Here a data based approach is proposed where the wave morphology in terms of Form Factor (FF) and R peak amplitude are calculated. Artificial Neural Network (ANN) is used for classification of PVC beats from normal ones. The obtained sensitivity (Se), specificity (Sp) and accuracy are 94.11% and 97.5% and 96.45 respectively.
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