Detection of the electrocardiogram P-wave using wavelet analysis

Abstract

Since wavelet analysis is an effective tool for analyzing transient signals, we studied its feature extraction and representation properties for events in electrocardiogram (EKG) data. Significant features of the EKG include the P-wave, the QRS complex, and the T-wave. For this paper the feature that we chose to focus on was the P-wave. Wavelet analysis was used as a pre-processor for a backpropagation neural network with conjugate gradient learning. The inputs to the neural network were the wavelet transforms of EKGs at a particular scale. The desired output was the location of the P-wave. The results were compared to results obtained without using the wavelet transform as a pre-processor. 1. INTRODUCTION The wavelet transform has emerged as an effective tool for analyzing transient signals with short-time behavior. Comparisons between the wavelet transform and more conventional methods such as the Fourier transform have been discussed extensively in the literature. 1,2,3 The local..