Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats

Abstract

In this paper, the supervised classification of the electrocardiogram (ECG) beats based on the fusion of several intelligent learning machines is described. For classification of ECG heartbeats, first, the QRS complexes are delineated by an efficient algorithm so as to identify the fiducial and J-locations of each complex. For each delineated QRS complex, a feature vector is established based on the geometrical properties of the complex waveform and its associated discrete-wavelet transform. Next, three different multi-layer perceptron back-propagation (MLP-BP) networks are trained with different topologies and intrinsic parameters. Afterwards, the outputs of MLP-BPs are used as the new feature space elements for training three adaptive fuzzy network inference systems (ANFIS) in order to increase the final accuracy. At the end, the outputs of ANFIS classifiers are voted based on majority for each input sample. The method was applied to seven arrhythmias (Normal, LBBB, RBBB, PVC, APB, VE, VF) which belong to the MIT-BIH Arrhythmia Database and the average accuracy value Acc = 98.28% was achieved for the beat-level. Also, the proposed method was assessed to five arrhythmias (Normal, LBBB, RBBB, PVC, APB) according to validation standards of the American Heart Association (AHA) at record (subject) level and the average accuracy value Acc = 73.39% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer-reviewed studies in this area.

• Adaptive network fuzzy inference system
• arrhythmia classification
• curve length method
• feature extraction
• fusion (hybrid) classification
• multi-layer perceptron
• supervised learning machine