HSAS: Heart Sound Authentication System

El-Bendary, Nashwa; Al-Qaheri, Hameed; Zawbaa, Hossam M.; Hamed, Mohamed; Hassanien, Aboul Ella; Zhao, Qiangfu; Abraham, Ajith

doi:10.1109/nabic.2010.5716306

Cited by 18 publications

(10 citation statements)

References 6 publications

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“…Proposed Heart Sound Authentication System (HSAS) was developed with an objective of using heart sounds as a biometric tool for human authentication [20]. It consists of five main phases: heart sound capturing, pre-processing, feature extraction, training, and classification and authentication phases.…”

Section: Methods Utilized In Heart Sounds Signal Classificationmentioning

confidence: 99%

Diagnostic spectral segregation of cardiac sounds features

Zeljković

Druzgalski

Bojic

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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Globally increasing rate of cardiovascular diseases represents one of the primary health care challenges and necessitates broader population screening for earlier intervention. Present cardiovascular function assessment techniques encompass a broad range of diagnostic tools which in most cases involve high and mid level cost instrumentation. They include among others instrumentation as a part of nuclear cardiology, cardiac catheterization, cardiac ultrasound, relatively basic multiple or simple channel ECG acquisition systems, and other. These techniques and related instrumentation represent very useful and powerful tools to diagnose and/or manage particular cardiac abnormalities; however they are not suitable for broader population low cost basic supplemental cardiac screening. Therefore, these studies focused on a structured assessment of cardiac sound traits as related auscultation is a part of standard medical exams and globally accepted procedures. For these reasons a dedicated cardiac sounds assessment algorithm was developed and has been tested on heart sound database that contains 565 signals encompassing 202 normal and 363 abnormal sounds. A broad range of diverse cardiac sound indicators, versatility of database, and derived characteristics validate potency of possible clinical usefulness and potential for a low cost broader diagnostic screening. In particular, the group of abnormal heart sounds contains samples of 35 different heart abnormalities which are divided into two groups including Group I that contains 28 different heart abnormalities that are 81.81% successfully detectable by the developed algorithm and Group II that contains 7 different heart abnormalities that do not exhibit the frequency change of S1 sound and are not classified by the proposed method. Normal heart sounds in 97.03% of cases were successfully classified with the developed method which is addressing the needs for a selected cardiac screening in light of increasing prevalence of cardiovascular diseases.

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Section: Methods Utilized In Heart Sounds Signal Classificationmentioning

confidence: 99%

Diagnostic spectral segregation of cardiac sounds features

Zeljković

Druzgalski

Bojic

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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“…By 2010, El-Bendary et al [3], used minimum distance classifier and multilayer perceptron classifier to improve the performance of an automated system to translate Arabic sign language into Arabic text.…”

Section: Related Workmentioning

confidence: 99%

Quranic Sign Language for Deaf People: Quranic Recitation Classification and Verification

Mahmod¹,

Zeki²

2018

IJPCC

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This paper provides an overview of the techniques used in image and video recognition for sign language through following hand motions and translating it to the text of the Holy Quran. It also provides a proposal for a system that will be capable of identifying errors in Quran recitation depending on alphabets of Arabic and Quranic sign language and be able to show where exactly errors have occurred. In addition, this system will identify and classify location of verse (Ayah) and names of Souras depending on Back Propagation technique of the neural network.

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“…A different approach of authenticating users is shown by El-Bendary et al [40] who use sounds of the beating heart. Though the signal is very hard to reproduce error rates are too high.…”

Section: Authentication Systems Overview 311 Conventional Authenticmentioning

confidence: 99%

Authentication Systems for Securing Clinical Documentation Workflows

Schwartze

Haarbrandt²,

Fortmeier³

et al. 2014

Methods Inf Med

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Integration of electronic signatures and timestamps to secure clinical archiving and KeywordsSecurity, digital signature, electronic signature, authentication system Summary Context: Integration of electronic signatures embedded in health care processes in Germany challenges health care service and supply facilities. The suitability of the signature level of an eligible authentication procedure is confirmed for a large part of documents in clinical practice. However, the concrete design of such a procedure remains unclear. Objective: To create a summary of usable user authentication systems suitable for clinical workflows.

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HSAS: Heart Sound Authentication System

Cited by 18 publications

References 6 publications

Diagnostic spectral segregation of cardiac sounds features

Diagnostic spectral segregation of cardiac sounds features

Quranic Sign Language for Deaf People: Quranic Recitation Classification and Verification

Authentication Systems for Securing Clinical Documentation Workflows

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