Performance Analysis Of Feature Extraction Schemes For ECG Signal Classification
Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. Electrocardiogram
is the most easily accessible bioelectric signal that provides the doctors with reasonably accurate data regarding the patient
heart condition. Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Normally ECG
related diagnoses are carried out manually. As the abnormal heart beats can occur randomly it becomes very tedious and
time-consuming to analyze say a 24 hour ECG signal, as it may contain hundreds of thousands of heart beats.
In this work we propose computer based automated system to help the doctor to detect cardiac arrhythmia. As
reference, we have used the Normal, Premature Ventricular Contraction (PVC) and Fusion signals of the MIT-BIH
Database. Then we have focused on the various schemes for extracting the useful features of the ECG signals for use with
artificial neural networks. We extract the principal characteristics of the signal by means of the Principal Component
Analysis (PCA) technique and other techniques such as Discrete Wavelet Transform and Discrete Cosine Transform. After
signal pre-processing, they are applied to an Artificial Neural Network Multilayer Perceptron (ANN MLP). The task of an
ANN based system is to correctly identify the three classes the feature extraction schemes are discussed and compared with
RBFN & Support Vector Machine in this work.