Human identification based on heart sound auscultation point

The application of human identification and verification has widely been used for over the past few decades. Drawbacks of such system however, are inevitable as forgery sophisticatedly developed alongside the technology advancement. Thus, this study proposed a research on the possibility of using he...

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Main Authors: Nur Fariza, I., Salleh, S. H., Numan, F., Hussain, H.
Format: Article
Published: Penerbit UTM Press 2017
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Online Access:http://eprints.utm.my/id/eprint/76688/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032261448&doi=10.11113%2fjt.v79.8320&partnerID=40&md5=2b6d0a0f330504e53081e9cda38e9331
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spelling my.utm.766882018-04-30T13:50:21Z http://eprints.utm.my/id/eprint/76688/ Human identification based on heart sound auscultation point Nur Fariza, I. Salleh, S. H. Numan, F. Hussain, H. QH301 Biology The application of human identification and verification has widely been used for over the past few decades. Drawbacks of such system however, are inevitable as forgery sophisticatedly developed alongside the technology advancement. Thus, this study proposed a research on the possibility of using heart sound as biometric. The main aim is to find an optimal auscultation point of heart sounds from either aortic, pulmonic, tricuspid or mitral that will most suitable to be used as the sound pattern for personal identification. In this study, the heart sound was recorded from 92 participants using a Welch Allyn Meditron electronic stethoscope whereas Meditron Analyzer software was used to capture the signal of heart sounds and ECG simultaneously for duration of 1 minute. The system is developed by a combination Mel Frequency Cepstrum Coefficients (MFCC) and Hidden Markov Model (HMM). The highest recognition rate is obtained at aortic area with 98.7% when HMM has 1 state and 32 mixtures, the lowest Equal Error Rate (EER) achieved was 0.9% which is also at aortic area. In contrast, the best average performance of HMM for every location is obtained at mitral area with 99.1% accuracy and 17.7% accuracy of EER at tricuspid area. Penerbit UTM Press 2017 Article PeerReviewed Nur Fariza, I. and Salleh, S. H. and Numan, F. and Hussain, H. (2017) Human identification based on heart sound auscultation point. Jurnal Teknologi, 79 (7). pp. 131-139. ISSN 0127-9696 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032261448&doi=10.11113%2fjt.v79.8320&partnerID=40&md5=2b6d0a0f330504e53081e9cda38e9331 DOI:10.11113/jt.v79.8320
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QH301 Biology
spellingShingle QH301 Biology
Nur Fariza, I.
Salleh, S. H.
Numan, F.
Hussain, H.
Human identification based on heart sound auscultation point
description The application of human identification and verification has widely been used for over the past few decades. Drawbacks of such system however, are inevitable as forgery sophisticatedly developed alongside the technology advancement. Thus, this study proposed a research on the possibility of using heart sound as biometric. The main aim is to find an optimal auscultation point of heart sounds from either aortic, pulmonic, tricuspid or mitral that will most suitable to be used as the sound pattern for personal identification. In this study, the heart sound was recorded from 92 participants using a Welch Allyn Meditron electronic stethoscope whereas Meditron Analyzer software was used to capture the signal of heart sounds and ECG simultaneously for duration of 1 minute. The system is developed by a combination Mel Frequency Cepstrum Coefficients (MFCC) and Hidden Markov Model (HMM). The highest recognition rate is obtained at aortic area with 98.7% when HMM has 1 state and 32 mixtures, the lowest Equal Error Rate (EER) achieved was 0.9% which is also at aortic area. In contrast, the best average performance of HMM for every location is obtained at mitral area with 99.1% accuracy and 17.7% accuracy of EER at tricuspid area.
format Article
author Nur Fariza, I.
Salleh, S. H.
Numan, F.
Hussain, H.
author_facet Nur Fariza, I.
Salleh, S. H.
Numan, F.
Hussain, H.
author_sort Nur Fariza, I.
title Human identification based on heart sound auscultation point
title_short Human identification based on heart sound auscultation point
title_full Human identification based on heart sound auscultation point
title_fullStr Human identification based on heart sound auscultation point
title_full_unstemmed Human identification based on heart sound auscultation point
title_sort human identification based on heart sound auscultation point
publisher Penerbit UTM Press
publishDate 2017
url http://eprints.utm.my/id/eprint/76688/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032261448&doi=10.11113%2fjt.v79.8320&partnerID=40&md5=2b6d0a0f330504e53081e9cda38e9331
_version_ 1643657382291570688
score 13.18916