Wavelet Transform Based Feature Extraction and Classification of Atrial Fibrillation Arrhythmia
Shipra Saraswat1, Geetika Srivastava2 and Shukla Sachchidanand N3

1Department of Computer Science, Amity University Uttar Pradesh India.

2Department of Electronics and Communication, Amity University Uttar Pradesh India.

3Dr. RML Avadh University, Department of Physics and Electronics, Faizabad, India.

Abstract: A new approach of automatic classification of atrial fibrillation (AF) arrhythmia is proposed in this paper. Our approach is based on discrete wavelet transform method followed by cross recurrence quantification analysis (CRQA) for extracting the features of experimental ECG signals. The features like laminarity, determinism, entropy, trapping time and transitivity are used for measuring the RQA measures. After that, the classification process has been performed using the concept of probabilistic neural network (PNN) approach. This method is applied to make a differentiation between normal persons and the persons having atrial fibrillation arrhythmia. For testing our approach PHYSIOBANK database of ECG signals have been used. The significance of this classification method has been shown in our Matlab generated results. The outcome of this paper will be very beneficial in treating AF patients. We achieved 100% accuracy by using this method.

Keywords: Atrial Fibrillation; Discrete Wavelet Transform; Cross Recurrence Quantification Analysis; Probabilistic Neural Network

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