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A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition
College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
College of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
* Author to whom correspondence should be addressed.
Received: 12 March 2013; in revised form: 12 April 2013 / Accepted: 14 May 2013 / Published: 22 May 2013
Abstract: In this paper, a human electrocardiogram (ECG) identification system based on ensemble empirical mode decomposition (EEMD) is designed. A robust preprocessing method comprising noise elimination, heartbeat normalization and quality measurement is proposed to eliminate the effects of noise and heart rate variability. The system is independent of the heart rate. The ECG signal is decomposed into a number of intrinsic mode functions (IMFs) and Welch spectral analysis is used to extract the significant heartbeat signal features. Principal component analysis is used reduce the dimensionality of the feature space, and the K-nearest neighbors (K-NN) method is applied as the classifier tool. The proposed human ECG identification system was tested on standard MIT-BIH ECG databases: the ST change database, the long-term ST database, and the PTB database. The system achieved an identification accuracy of 95% for 90 subjects, demonstrating the effectiveness of the proposed method in terms of accuracy and robustness.
Keywords: biometrics; ECG Identification System; ensemble empirical mode decomposition; k-nearest neighbors
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Cite This Article
MDPI and ACS Style
Zhao, Z.; Yang, L.; Chen, D.; Luo, Y. A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition. Sensors 2013, 13, 6832-6864.
Zhao Z, Yang L, Chen D, Luo Y. A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition. Sensors. 2013; 13(5):6832-6864.
Zhao, Zhidong; Yang, Lei; Chen, Diandian; Luo, Yi. 2013. "A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition." Sensors 13, no. 5: 6832-6864.