期刊
SENSORS
卷 13, 期 5, 页码 6832-6864出版社
MDPI
DOI: 10.3390/s130506832
关键词
biometrics; ECG Identification System; ensemble empirical mode decomposition; k-nearest neighbors
资金
- National Natural Science Foundation of China [61102133]
- Key Project of the Science Technology Department of Zhejiang Province [2010C11065]
- Science & Technology Innovation Team of Zhejiang Province [2012R10010-15]
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.
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