title: Neural network and wavelet average framing percentage energy for atrial fibrillation classification.
abstract: ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study.
authors: Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia. Electronic address: haleddaq@yahoo.com.
authors: K
authors: K
authors: Daqrouq
authors: Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia. Electronic address: drkhateeb@kau.edu.sa.
authors: A
authors: A
authors: Alkhateeb
authors: Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia. Electronic address: mohammedajour@yahoo.co.uk.
authors: M N
authors: MN
authors: Ajour
authors: Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia. Electronic address: morfeq@kau.edu.sa.
authors: A
authors: A
authors: Morfeq
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001281:Atrial Fibrillation / Q000145:classification* / Q000175:diagnosis*; D019295:Computational Biology; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D004562:Electrocardiography / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012985:Software Design; D058067:Wavelet Analysis
keywords: Atrial fibrillation; Average framing; Noise; Percentage energy; Probabilistic neural network; Wavelet