Compressed ECG biometric using cardioid graph based feature extraction
In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompressi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/46654/1/jeas_1215_3154.pdf http://irep.iium.edu.my/46654/ http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1215_3154.pdf |
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| Summary: | In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompression of the compressed ECG signal is necessary before the recognition step. The QRS complexes obtained from the ECG signal is compressed using Discrete Wavelet Transform (DWT), followed by the Cardioid graph retrieval procedure. Compression is performed in three decomposition levels and with the first two Daubechies wavelets. Classification is conducted on all the three levels using Multilayer Perceptron (MLP) Neural Network. Maximum compression of 87.5% is achieved with an accuracy rate of
93.75%. For compression rate of 85%, the identification rate obtained is 98.75%. The same highest recognition rate of 98.75% is attained both with non-compressed and compressed data. The classification accuracy rates suggest that compressed ECG biometric with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system. |
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