Stetho-phone: Low-cost digital stethoscope for remote personalized healthcare

Fattah, Shaikh Anowarul; Rahman, Nael Mizanur; Maksud, Ahmed; Foysal, Shariful Islam; Chowdhury, Rakibul Islam; Chowdhury, Sayeed Shafayet; Shahanaz, Celia

doi:10.1109/ghtc.2017.8239325

Cited by 18 publications

(16 citation statements)

References 7 publications

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“…The main tool used to pick up signals was the digital stethoscope, mentioning in 44 articles (IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,44,45,46,48,49,50,51,52,53,54,56). The connectivity of digital stethoscopes occurs, in most equipment, by Bluetooth, not over the Internet, and for this reason these equipments are not considered IoT devices.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites¹,

Silva²,

Barbosa³

et al. 2021

Preprint

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This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

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Section: Resultsmentioning

confidence: 99%

“…Fattah et al [45] (5) proposed a low-cost digital stethoscope for remote monitoring and the use of machine learning algorithms. The use of this equipment is primarily intended for personnel monitoring in hard-to-reach locations.…”

Section: Sq1-what Is the Distribution Of Articles Considering Countries?mentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites¹,

Silva²,

Barbosa³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In this way, IoT can improve the effectiveness of treatments, prevent risk situations and assist in health promotion. Twelve studies approached the context of remote monitoring (IDs = 5, 9, 10, 17,27,36,38,40,43,44,51,55). In addition, IoT increases resource management efficiency through flexibility and mobility using intelligent solutions.…”

Section: Gq2-what Are the Benefits For The Patient By Using Iot For Cardiac Care?mentioning

confidence: 99%

“…Among the 58 articles selected, 34 works (58.62%, IDs = 1, 2, 5, 6, 10, 19,20,21,22,25,31,32,33,34,35,36,37,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,58) mention care using Machine Learning. Leng et al [30] (36) showed the possible interactions between the electronic stethoscope, the sensor-captured signal decomposition algorithm, machine learning techniques and cardiac sound segmentation.…”

Section: Fq1-is There Mention Of Care Using Machine Learning?mentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

et al. 2021

View full text Add to dashboard Cite

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

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“…To that end, one potential domain is the usage of biosensors [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. They are preferable due to high selectivity and sensitivity, rapid response time, cost-effectiveness, and multiplexing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of a Highly Sensitive Surface Plasmon Resonance Sensor for SARS-CoV-2 Detection

2021

Self Cite

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In this paper, we propose a surface plasmon resonance (SPR) structure based on Kretschmann configuration incorporating layers of silicon and BaTiO 3 on top of Ag for real-time detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using thiol-tethered DNA as a ligand. Extensive numerical analysis based on transfer matrix theory as well as finite-difference time-domain (FDTD) technique has been performed to characterize the sensor response considering sensitivity, full width at half maxima, and minimum reflection. About 7.6 times enhanced sensitivity has been obtained using the proposed architecture for SARS-CoV-2 detection, compared to the basic Kretschmann configuration. Notably, the structure provides consistent enhancement over other competitive SPR structures for both angular and wavelength interrogations with a figure-of-merit of 692.28. Additionally, we repeated simulations for various ligate-ligand pairs to assess the range of applicability and robust performance improvement has been observed. As a result, the proposed sensor design provides a suitable configuration for highly sensitive, rapid, noninvasive biosensing which can be useful if adopted in experimental sensing protocols.

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Stetho-phone: Low-cost digital stethoscope for remote personalized healthcare

Cited by 18 publications

References 7 publications

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Numerical Analysis of a Highly Sensitive Surface Plasmon Resonance Sensor for SARS-CoV-2 Detection

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