Development of an acceleration plethysmogram based cardioid graph biometric identification

The increasing identity theft cases are alarming which puts biometric as the alternative solution to combat identity crime. Recently, biosignals are proposed as biometric modalities. Thus, in this study, the development of an Acceleration Plethysmogram (APG) based Cardioid graph biometric identif...

Full description

Bibliographic Details
Main Authors:	Sidek, Khairul Azami, Osman, Munieroh, Azam, Siti Nurfarah Ain, Zainal, Nur Izzati
Format:	Article
Language:	English English
Published:	Science & Engineering Research Support Society 2016
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/51231/ http://irep.iium.edu.my/51231/ http://irep.iium.edu.my/51231/1/IJBSBTvol8no32016.pdf http://irep.iium.edu.my/51231/4/51231-Development_of_an_acceleration_plethysmogram_based_cardioid_SCOPUS.pdf

id	iium-51231
recordtype	eprints
spelling	iium-512312016-11-25T03:42:11Z http://irep.iium.edu.my/51231/ Development of an acceleration plethysmogram based cardioid graph biometric identification Sidek, Khairul Azami Osman, Munieroh Azam, Siti Nurfarah Ain Zainal, Nur Izzati TK7885 Computer engineering The increasing identity theft cases are alarming which puts biometric as the alternative solution to combat identity crime. Recently, biosignals are proposed as biometric modalities. Thus, in this study, the development of an Acceleration Plethysmogram (APG) based Cardioid graph biometric identification is presented. A total of 10 Photoplethysmogram (PPG) data from MIMIC II Waveform Database (MIMIC2WDB) with sampling frequency of 125 Hz were obtained. The datasets are later converted to APG signal by the second order differentiation and preprocessed with Butterworth filter. Then, Cardioid based graph of APG signal was generated. Its centroid and Euclidean distance are calculated. Finally, classification is done by applying these extracted features to Multilayer Perceptron (MLP) and Naïve Bayes neural networks classifiers. Our experimentation results show that subject recognition is possible by obtaining classification accuracy of 95% for APG based Cardioid graph for both classifiers while only 85% and 70% for PPG signal in MLP and Naïve Bayes classifiers. These outcomes indicate that APG based Cardioid graph biometric identification is a feasible solution to overcome identity fraud. Science & Engineering Research Support Society 2016 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51231/1/IJBSBTvol8no32016.pdf application/pdf en http://irep.iium.edu.my/51231/4/51231-Development_of_an_acceleration_plethysmogram_based_cardioid_SCOPUS.pdf Sidek, Khairul Azami and Osman, Munieroh and Azam, Siti Nurfarah Ain and Zainal, Nur Izzati (2016) Development of an acceleration plethysmogram based cardioid graph biometric identification. International Journal of Bio-Science and Bio-Technology, 8 (3). pp. 9-20. ISSN 2233-7849 http://www.sersc.org/journals/IJBSBT/vol8_no3/2.pdf
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 Sidek, Khairul Azami Osman, Munieroh Azam, Siti Nurfarah Ain Zainal, Nur Izzati Development of an acceleration plethysmogram based cardioid graph biometric identification
description	The increasing identity theft cases are alarming which puts biometric as the alternative solution to combat identity crime. Recently, biosignals are proposed as biometric modalities. Thus, in this study, the development of an Acceleration Plethysmogram (APG) based Cardioid graph biometric identification is presented. A total of 10 Photoplethysmogram (PPG) data from MIMIC II Waveform Database (MIMIC2WDB) with sampling frequency of 125 Hz were obtained. The datasets are later converted to APG signal by the second order differentiation and preprocessed with Butterworth filter. Then, Cardioid based graph of APG signal was generated. Its centroid and Euclidean distance are calculated. Finally, classification is done by applying these extracted features to Multilayer Perceptron (MLP) and Naïve Bayes neural networks classifiers. Our experimentation results show that subject recognition is possible by obtaining classification accuracy of 95% for APG based Cardioid graph for both classifiers while only 85% and 70% for PPG signal in MLP and Naïve Bayes classifiers. These outcomes indicate that APG based Cardioid graph biometric identification is a feasible solution to overcome identity fraud.
format	Article
author	Sidek, Khairul Azami Osman, Munieroh Azam, Siti Nurfarah Ain Zainal, Nur Izzati
author_facet	Sidek, Khairul Azami Osman, Munieroh Azam, Siti Nurfarah Ain Zainal, Nur Izzati
author_sort	Sidek, Khairul Azami
title	Development of an acceleration plethysmogram based cardioid graph biometric identification
title_short	Development of an acceleration plethysmogram based cardioid graph biometric identification
title_full	Development of an acceleration plethysmogram based cardioid graph biometric identification
title_fullStr	Development of an acceleration plethysmogram based cardioid graph biometric identification
title_full_unstemmed	Development of an acceleration plethysmogram based cardioid graph biometric identification
title_sort	development of an acceleration plethysmogram based cardioid graph biometric identification
publisher	Science & Engineering Research Support Society
publishDate	2016
url	http://irep.iium.edu.my/51231/ http://irep.iium.edu.my/51231/ http://irep.iium.edu.my/51231/1/IJBSBTvol8no32016.pdf http://irep.iium.edu.my/51231/4/51231-Development_of_an_acceleration_plethysmogram_based_cardioid_SCOPUS.pdf
first_indexed	2023-09-18T21:12:31Z
last_indexed	2023-09-18T21:12:31Z
_version_	1777411346792448000

Development of an acceleration plethysmogram based cardioid graph biometric identification

Similar Items