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...

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Main Authors: Waili, Tuerxun, Mohd Nor, Rizal, Sidek, Khairul Azami, Abdul Rahman, Abdul Wahab, Guven, Ghokan
Format: Conference or Workshop Item
Language:English
English
Published: Springer International Publishing 2017
Subjects:
Online Access: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
id iium-60819
recordtype eprints
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 Conference or Workshop Item 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
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic TK7885 Computer engineering
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
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.
format Conference or Workshop Item
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
title Real time electrocardiogram identification with multi-modal machine learning algorithms
title_short 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_sort real time electrocardiogram identification with multi-modal machine learning algorithms
publisher Springer International Publishing
publishDate 2017
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
first_indexed 2023-09-18T21:26:13Z
last_indexed 2023-09-18T21:26:13Z
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