الفهرس 
Medical BackgroundOnly 14 pages are availabe for public view
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Abstract
Abstract
The Electrocardiogram (ECG) has been introduced for decades as a powerful tool for diagnosing heart diseases. Hence, the automation of analyzing a rich source of information like ECG for diagnostic purposes is very crucial, since it helps 24-hour monitoring and instant discovering of cardiac disorders which need rapid medical aid in clinical situations.
Cardiac arrhythmias mean abnormal activities in the heart upon certain conditions and mainly consist of two types. One of them is life threatening and can cause death. On the other hand, the other type is cardiac arrhythmia which is our interest in this study. It needs attention to avoid deterioration, but it is not critical as life threatening as the first one. Thus, ECG heartbeats should be continuously examined and classified.
This thesis proposes an automatic reliable two-stage hybrid hierarchical method for ECG heartbeat classification. The heartbeats are segmented dynamically to avoid the consequences of the heart rate variability. Discrete Wavelet Transform (DWT) is utilized to extract morphological features that describe the segmented heartbeat. The extracted features are then reduced by using Principal Component Analysis (PCA). Subsequently, the resulted features along with four RR features are fed into a Support Vector Machine (SVM) to classify five categories (first stage). Thereafter, the heartbeats are further classified to one of the classes belonging to the assigned category (second stage). Two different strategies for classification have been investigated: One versus All and One versus One. The proposed method has been applied on data from lead 1 and lead 2. A new fusion step is introduced, where a stacked generalization algorithm is applied and different types of classifiers have been examined. Experiments have been carried out using MIT-BIH database. The best overall and average accuracies obtained by the first stage are 98.40% and 97.50% respectively. For the second stage, 94.94% and 93.19% are the best overall and average accuracies obtained respectively. The best results are achieved using SVM with One versus One classification strategy for both stages and decision trees classifier for the fusion step.