Person identification in irregular cardiac conditions using electrocardiogram signals

Person identification in irregular cardiac conditions using electrocardiogram signals

This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challeng...

Full description

Bibliographic Details
Main Authors: Sidek, Khairul Azami, Khalil, Ibrahim
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://irep.iium.edu.my/21810/
http://irep.iium.edu.my/21810/
http://irep.iium.edu.my/21810/1/Person_identification_in_irregular_cardiac_conditions_using_electrocardiogram_signals.pdf
id iium-21810
recordtype eprints
spelling iium-218102012-03-14T02:48:44Z http://irep.iium.edu.my/21810/ Person identification in irregular cardiac conditions using electrocardiogram signals Sidek, Khairul Azami Khalil, Ibrahim TJ Mechanical engineering and machinery This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challenge database (AFPDB), MIT-BIH Supraventricular Arrthymia database (SVDB) and T-Wave Alternans Challenge database (TWADB). Cross correlation (CC) was used as the biometric matching algorithm with defined threshold values to evaluate the performance. In order to measure the efficiency of this simple yet effective matching algorithm, two biometric performance metrics were used which are false acceptance rate (FAR) and false reject rate (FRR). Our experimentation results suggest that ECG based biometric identification with irregular cardiac condition gives a higher recognition rate of different ECG signals when tested for three different abnormal cardiac databases yielding false acceptance rate (FAR) of 2%, 3% and 2% and false reject rate (FRR) of 1%, 2% and 0% for AFPDB, SVDB and TWADB respectively. These results also indicate the existence of salient biometric characteristics in the ECG morphology within the QRS complex that tends to differentiate individuals. 2011-12-01 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/21810/1/Person_identification_in_irregular_cardiac_conditions_using_electrocardiogram_signals.pdf Sidek, Khairul Azami and Khalil, Ibrahim (2011) Person identification in irregular cardiac conditions using electrocardiogram signals. In: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011, 30 Aug.-3 Sept. 2011, Boston, MA. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6090644
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Sidek, Khairul Azami
Khalil, Ibrahim
Person identification in irregular cardiac conditions using electrocardiogram signals
description This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challenge database (AFPDB), MIT-BIH Supraventricular Arrthymia database (SVDB) and T-Wave Alternans Challenge database (TWADB). Cross correlation (CC) was used as the biometric matching algorithm with defined threshold values to evaluate the performance. In order to measure the efficiency of this simple yet effective matching algorithm, two biometric performance metrics were used which are false acceptance rate (FAR) and false reject rate (FRR). Our experimentation results suggest that ECG based biometric identification with irregular cardiac condition gives a higher recognition rate of different ECG signals when tested for three different abnormal cardiac databases yielding false acceptance rate (FAR) of 2%, 3% and 2% and false reject rate (FRR) of 1%, 2% and 0% for AFPDB, SVDB and TWADB respectively. These results also indicate the existence of salient biometric characteristics in the ECG morphology within the QRS complex that tends to differentiate individuals.
format Conference or Workshop Item
author Sidek, Khairul Azami
Khalil, Ibrahim
author_facet Sidek, Khairul Azami
Khalil, Ibrahim
author_sort Sidek, Khairul Azami
title Person identification in irregular cardiac conditions using electrocardiogram signals
title_short Person identification in irregular cardiac conditions using electrocardiogram signals
title_full Person identification in irregular cardiac conditions using electrocardiogram signals
title_fullStr Person identification in irregular cardiac conditions using electrocardiogram signals
title_full_unstemmed Person identification in irregular cardiac conditions using electrocardiogram signals
title_sort person identification in irregular cardiac conditions using electrocardiogram signals
publishDate 2011
url http://irep.iium.edu.my/21810/
http://irep.iium.edu.my/21810/
http://irep.iium.edu.my/21810/1/Person_identification_in_irregular_cardiac_conditions_using_electrocardiogram_signals.pdf
first_indexed 2023-09-18T20:33:16Z
last_indexed 2023-09-18T20:33:16Z
_version_ 1777408877975830528

Similar Items