Neuro-ANFIS Architecture for ECG Rhythm-Type Recognition Using Different QRS Geometrical-based Features

The paper addresses a new QRS complex geometrical feature extractiontechnique as well as its application for electrocardiogram (ECG) supervised hybrid (fusion)beat-type classification. To this end, after detection and delineation of the major events of ECG signal via a robust algorithm, each QRS region and also its corresponding discretewavelet transform (DWT) are supposed as virtual images and each of them is divided intoeight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposedclassification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structureconsisting of three Multi Layer Perceptron-Back Propagation (MLP-BP) neural networkswith different topologies and one Adaptive Network Fuzzy Inference System (ANFIS)were designed and implemented. To show the merit of the new proposed algorithm, it was applied to all MIT-BIH Arrhythmia Database records and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result,the average accuracy value Acc=98.27% was obtained. Also, the proposed method wasapplied to 8 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, VE, PB, VF)belonging to 19 number of the aforementioned database and the average value ofAcc=98.08% was achieved. To evaluate performance quality of the new proposed hybridlearning machine, the obtained results were compared with similar peer-reviewed studies in this area.