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Open AccessArticle

A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition

1
College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2
College of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
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Author to whom correspondence should be addressed.
Sensors 2013, 13(5), 6832-6864; https://doi.org/10.3390/s130506832
Received: 12 March 2013 / Revised: 12 April 2013 / Accepted: 14 May 2013 / Published: 22 May 2013
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: biometrics; ECG Identification System; ensemble empirical mode decomposition; k-nearest neighbors biometrics; ECG Identification System; ensemble empirical mode decomposition; k-nearest neighbors
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.

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