Performance evaluation of heart sounds biometric systems on an open dataset

Spadaccini, Andrea; Beritelli, Francesco

doi:10.1109/icdsp.2013.6622835

Cited by 28 publications

(32 citation statements)

References 12 publications

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“…It contains heart sounds acquired from 206 people, i.e. , 157 male and 49 female [20,21]. Eighty people (65 male and 15 female) are selected randomly in our experiments.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

Heart Sound Biometric System Based on Marginal Spectrum Analysis

Zhao

Shen

Ren

2013

Sensors

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This work presents a heart sound biometric system based on marginal spectrum analysis, which is a new feature extraction technique for identification purposes. This heart sound identification system is comprised of signal acquisition, pre-processing, feature extraction, training, and identification. Experiments on the selection of the optimal values for the system parameters are conducted. The results indicate that the new spectrum coefficients result in a significant increase in the recognition rate of 94.40% compared with that of the traditional Fourier spectrum (84.32%) based on a database of 280 heart sounds from 40 participants.

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“…It contains heart sounds acquired from 206 people, i.e. , 157 male and 49 female [20,21]. Eighty people (65 male and 15 female) are selected randomly in our experiments.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

Heart Sound Biometric System Based on Marginal Spectrum Analysis

Zhao

Shen

Ren

2013

Sensors

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“…• environmental monitoring (air quality, rain, outdoor and internal temperature sensors) [ • data network monitoring [22].…”

Section: E Innovative Sensorsmentioning

confidence: 99%

SMILE: Smart Monitoring IoT Learning Ecosystem

Avanzato

Beritelli

Franco³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results. Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.

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“…Özellikle kişilerin kas ve kemik yapısı, vücudun fiziksel ve morfolojik yapısı kalp seslerinin çeşitliliğini sağlamaktadır. Bu çeşitlilik kalp seslerinin kişi tanımada biyometrik olarak kullanılmasını sağlamaktadır [5,6]. Bir kişiden alınan kalp sesi örnek olarak Şekil-1 de gösterilmektedir.…”

Section: Kalbi̇n çAlişma Prensi̇bi̇ Ve Kalp Sesleri̇unclassified

“…Deneysel çalışmaları yapmak için kullandığımız veri tabanı literatürde [6]' de kullanılan veri tabanıdır. Bu çalışma için, veri tabanında bulunan 98 (49 bayan, 49 erkek) kişiye ait kalp sesleri kullanılmıştır.…”

Section: A Veri Tabanıunclassified

Gender detection with heart sound

Dal

Coşğun

Özbek

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Bu çalışmada, her bir bireye ait kalp sesleri kullanılarak cinsiyet tanıma işlemi gerçekleştirilmiştir. Kalp sesi ile cinsiyet tanıma yapmak için önerilen yöntem üç aşamadan oluşmaktadır. İlk aşamada kalp seslerinden öznitelikler çıkartılır. İkinci aşamada çıkartılan öznitelikler kullanılarak Gauss Karışım Modeli (GKM) ile modelleme yapılır ve bu modeller eğitilir. Son aşamada ise olabilirlik oranı testi ile test edilen kişinin erkek ya da bayan olduğuna karar verilir. Anahtar Kelimeler -kalp sesi; Gauss Karışım Modeli; olabilirlik oranı testi; cinsiyet tanıma.Abstract-In this work, gender detection was carried out using heart sounds of each persons. The proposed method has three stages to make detection gender with heart sound. First, some features are obtained from heart sounds. Second, modelling is made by Gauss Mixture Model (GMM) by using obtained these features and models are trained. Finally, tested person is decided to be he or she with likelihood ratio test.

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Performance evaluation of heart sounds biometric systems on an open dataset

Cited by 28 publications

References 12 publications

Heart Sound Biometric System Based on Marginal Spectrum Analysis

Heart Sound Biometric System Based on Marginal Spectrum Analysis

SMILE: Smart Monitoring IoT Learning Ecosystem

Gender detection with heart sound

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