Real time electrocardiogram identification with multi-modal machine learning algorithms
Weaknesses in conventional identification technologies such as identification cards, badges and RFID tags prompts attention to biometric form of identification. Biometrics like voice, brain signal and finger print are unique human traits that can be used for identification. In this paper we prese...
| Main Authors: | , , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
Springer International Publishing
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/60819/ http://irep.iium.edu.my/60819/1/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal.pdf http://irep.iium.edu.my/60819/7/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal_WOS.pdf |
| _version_ | 1848785555015335936 |
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| author | Waili, Tuerxun Mohd Nor, Rizal Sidek, Khairul Azami Abdul Rahman, Abdul Wahab Guven, Ghokan |
| author_facet | Waili, Tuerxun Mohd Nor, Rizal Sidek, Khairul Azami Abdul Rahman, Abdul Wahab Guven, Ghokan |
| author_sort | Waili, Tuerxun |
| building | IIUM Repository |
| collection | Online Access |
| description | Weaknesses in conventional identification technologies such as
identification cards, badges and RFID tags prompts attention to biometric form
of identification. Biometrics like voice, brain signal and finger print are unique
human traits that can be used for identification. In this paper we present an
identification system based on Electrocardiogram (heart signal). There is a
considerable number of research in the past with high accuracy for identification,
however, most ignore the practical time required to identify an individual.
In this study, we explored a more practical approach in identification by
reducing the number of time required for identification. We explore ways to
identity a person within 3–4 s using just 5 heart beats. We extracted few reliable
features from each QRS complexes, combined effort of three algorithms to
achieve 96% accuracy. This approach is more suitable and practical in real time
applications where time for identification is important. |
| first_indexed | 2025-11-14T16:55:00Z |
| format | Proceeding Paper |
| id | iium-60819 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:55:00Z |
| publishDate | 2017 |
| publisher | Springer International Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-608192019-02-20T08:09:05Z http://irep.iium.edu.my/60819/ Real time electrocardiogram identification with multi-modal machine learning algorithms Waili, Tuerxun Mohd Nor, Rizal Sidek, Khairul Azami Abdul Rahman, Abdul Wahab Guven, Ghokan TK7885 Computer engineering Weaknesses in conventional identification technologies such as identification cards, badges and RFID tags prompts attention to biometric form of identification. Biometrics like voice, brain signal and finger print are unique human traits that can be used for identification. In this paper we present an identification system based on Electrocardiogram (heart signal). There is a considerable number of research in the past with high accuracy for identification, however, most ignore the practical time required to identify an individual. In this study, we explored a more practical approach in identification by reducing the number of time required for identification. We explore ways to identity a person within 3–4 s using just 5 heart beats. We extracted few reliable features from each QRS complexes, combined effort of three algorithms to achieve 96% accuracy. This approach is more suitable and practical in real time applications where time for identification is important. Springer International Publishing 2017 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/60819/1/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal.pdf application/pdf en http://irep.iium.edu.my/60819/7/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal_WOS.pdf Waili, Tuerxun and Mohd Nor, Rizal and Sidek, Khairul Azami and Abdul Rahman, Abdul Wahab and Guven, Ghokan (2017) Real time electrocardiogram identification with multi-modal machine learning algorithms. In: 2nd International Conference of Reliable Information and Communication Technology (IRICT) 2017, 23rd-24th April 2017, Johor Bahru, Johor. https://link.springer.com/chapter/10.1007/978-3-319-59427-9_48 |
| spellingShingle | TK7885 Computer engineering Waili, Tuerxun Mohd Nor, Rizal Sidek, Khairul Azami Abdul Rahman, Abdul Wahab Guven, Ghokan Real time electrocardiogram identification with multi-modal machine learning algorithms |
| title | Real time electrocardiogram identification with multi-modal machine learning algorithms |
| title_full | Real time electrocardiogram identification with multi-modal machine learning algorithms |
| title_fullStr | Real time electrocardiogram identification with multi-modal machine learning algorithms |
| title_full_unstemmed | Real time electrocardiogram identification with multi-modal machine learning algorithms |
| title_short | Real time electrocardiogram identification with multi-modal machine learning algorithms |
| title_sort | real time electrocardiogram identification with multi-modal machine learning algorithms |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/60819/ http://irep.iium.edu.my/60819/ http://irep.iium.edu.my/60819/1/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal.pdf http://irep.iium.edu.my/60819/7/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal_WOS.pdf |