부산대학교 도서관

The research presented in this paper describes an identification method based on the electrocardiogram (ECG) signal, using the K-Nearest Neighbors (Knn) identification method and the fractional order system of commensurate order (SOF) as a modeling tool. There are two sections to our approach. A fractional identification model was used in the first section to model the QRS complex of the ECG signal, and the biometric identification method was tested using the MIT/BIH databases for normal and arrhythmic data. In the second section, pertinent fractional modeling parameters were applied to the biometric identification system. Using the Knn classification algorithm, the suggested identification approach attained an accuracy rate of 100%.