Cardiac Abnormalities Classification Using Principal Component Analysis

Cardiac patients require long term monitoring of Electrocardiogram signals. However, it is a very tedious and time-consuming task to analyze the ECG recording beat by beat in a long-term monitoring. This is because the abnormal heart beats can occur randomly and a long-term ECG record, say 24 hours, may contain hundreds of thousands of beats. Hence, it is highly desirable to automate the entire process of ECG classification. The present work proposes a technique for ECG classification. First, the ECG signals belonging to each category were extracted from the MIT-BIH arrhythmia database features are extracted from the ECG signal using Principal Component Analysis (PCA) .This process drastically reduces the dimensionality of the vectors to be classified. The feature vectors thus obtained are used to train a neural network (NN) classifier. After the network is trained, its performance in terms of its generalizing ability is tested on a separate test dataset which was not used during training. The outcome showed that the FF neural network performance is better.