Cardioid graph based ECG biometric using compressed QRS complex
In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric at different physiological conditions. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of...
| Main Authors: | , |
|---|---|
| Format: | Proceeding Paper |
| Language: | English |
| Published: |
IEEE
2015
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/46347/ http://irep.iium.edu.my/46347/1/46347.pdf |
| _version_ | 1848782952646836224 |
|---|---|
| author | Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami |
| author_facet | Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami |
| author_sort | Iqbal, Fatema-tuz-Zohra |
| building | IIUM Repository |
| collection | Online Access |
| description | In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric at different physiological conditions. 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 three Daubechies wavelets. Classification is conducted on all the three
levels using Multilayer Perceptron (MLP) Neural Network.
Maximum compression of 88.3% is achieved with an accuracy
rate of 93.06%. For compression rate of 85%, the identification rate obtained is 95.3%. Highest recognition rate of 96.4% is attained when the compression ratio is 75%. The classification accuracy rates suggest that compressed ECG biometric in varying physiological conditions with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system. |
| first_indexed | 2025-11-14T16:13:38Z |
| format | Proceeding Paper |
| id | iium-46347 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:13:38Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-463472020-10-30T07:59:16Z http://irep.iium.edu.my/46347/ Cardioid graph based ECG biometric using compressed QRS complex Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami TK7885 Computer engineering In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric at different physiological conditions. 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 three Daubechies wavelets. Classification is conducted on all the three levels using Multilayer Perceptron (MLP) Neural Network. Maximum compression of 88.3% is achieved with an accuracy rate of 93.06%. For compression rate of 85%, the identification rate obtained is 95.3%. Highest recognition rate of 96.4% is attained when the compression ratio is 75%. The classification accuracy rates suggest that compressed ECG biometric in varying physiological conditions with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system. IEEE 2015 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/46347/1/46347.pdf Iqbal, Fatema-tuz-Zohra and Sidek, Khairul Azami (2015) Cardioid graph based ECG biometric using compressed QRS complex. In: 2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS 2015), 26th-28th May 2015, Kuala Lumpur. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7292209 10.1109/ICBAPS.2015.7292209 |
| spellingShingle | TK7885 Computer engineering Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami Cardioid graph based ECG biometric using compressed QRS complex |
| title | Cardioid graph based ECG biometric using compressed QRS complex |
| title_full | Cardioid graph based ECG biometric using compressed QRS complex |
| title_fullStr | Cardioid graph based ECG biometric using compressed QRS complex |
| title_full_unstemmed | Cardioid graph based ECG biometric using compressed QRS complex |
| title_short | Cardioid graph based ECG biometric using compressed QRS complex |
| title_sort | cardioid graph based ecg biometric using compressed qrs complex |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/46347/ http://irep.iium.edu.my/46347/ http://irep.iium.edu.my/46347/ http://irep.iium.edu.my/46347/1/46347.pdf |