Arduino Based Cost-Effective Design and Development of a Digital Stethoscope

Waqar, Muhammad Umer; Inam, Shehla; Rehman, Muhammad Azeem ur; Ishaq, Muhammad; Afzal, Mafia; Tariq, Nimra; Amin, Faisal; Qurat-ul-Ain,

doi:10.1109/icet48972.2019.8994674

Cited by 15 publications

(18 citation statements)

References 7 publications

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“…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 9 https://physionet.org/ through flexibility and mobility using intelligent solutions.…”

Section: Resultsmentioning

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%

“…Ren et al [42] (2) used convolutional neural networks (CNN) (19 articles cite CNN, IDs = 1, 2, 10, 19,23,31,32,33,39,41,43,46,47,49,51,52,54,55,58) for classification of PCG scale images for the task of classifying cardiac sounds between normal and abnormal. PCG signal representations were obtained by dataset from: Massachusetts Institute of Technology (MIT), Aalborg University, Aristotle University of Thessaloniki, University of Haute Alsace, Dalian University of Technology, and Shiraz University.…”

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

confidence: 99%

“…Maritsch et al [16] (1) reported the world population's increased use of smartwatches for cardiac monitoring. Frank and Meng [44] (4), Szot et al [46] (6) and Waqar et al [43] (3) proposed the creation of low-cost digital stethoscopes based on Arduino. The articles of the cited authors present the use of low-cost devices to monitor and support the analysis of signals produced by the human heart.…”

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

confidence: 99%

“…Articles that present smart stethoscope designs with bluetooth communication ([50](10)), cloud signal storage (14 articles, IDs = 5, 19,32,36,40,41,43,44,46,48,49,51,55,56), in order to improve signal amplification ([57] (17)), using signal processing by MatLab ([58] (18)), enabling solutions via ubiquitous computing ([73](38), [78] (44)).…”

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

confidence: 99%

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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%

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

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

Brites

Silva

Barbosa

et al. 2022

Lecture Notes in Networks and Systems

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CORELSA - Remote stethoscope system for fast and standardized auscultations of large numbers of patients with respiratory syndromes

Ides,

Aerts,

Baeyens

et al. 2024

Preprint

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One of the major impacts of the current COVID-19 pandemic is the immense strain that is being put on the intensive care units. As a typical SARS-CoV-2 infection often leads to severe acute respiratory syndromes, one of the most basic essential examinations is lung auscultation. There are different categories of lung sounds that can be assessed with a stethoscope like crackles, wheezes, rhonchi and coarse breath sounds. Each of these malicious lung sounds contribute to a clinical assessment and follow-up of the patient's airways and lungs and can indicate the development of pneumonia. Therefore, auscultation on a regular basis is essential, but it also takes a lot of time for the physicians. Moreover, because of the risk of infection for health care workers, it is becoming increasingly difficult or sometimes impossible to safely use a stethoscope. Here we introduce a remote stethoscope system that can help to reduce the workload of physicians and nurses, diminishes the risk of contamination and leads to more standardized auscultation, which can also be stored and reevaluated. We provide a detailed description of our system, which consists of parts that are low cost, easily available in most parts of the world, or can be 3D printed. We provide instructions and an open source software stack to run such a system for a large number of patients which can be remotely auscultated from a central computer. We believe that the system described in this paper can allow various institutes to replicate the system, and increase the safety and reduce the overall workload in the intensive care units around the world.

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Arduino Based Cost-Effective Design and Development of a Digital Stethoscope

Cited by 15 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

CORELSA - Remote stethoscope system for fast and standardized auscultations of large numbers of patients with respiratory syndromes

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