Literature DB >> 33436354

A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study.

Chuan Yang1, Wei Zhang2, Zhixuan Pang3, Jing Zhang4, Deling Zou1, Xinzhong Zhang1, Sicong Guo1, Jiye Wan1, Ke Wang5, Wenyue Pang1.   

Abstract

BACKGROUND: Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients.
OBJECTIVE: The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope.
METHODS: We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope.
RESULTS: We were able to conveniently operate Auscul Pi and precisely record the patients' auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001).
CONCLUSIONS: Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19. TRIAL REGISTRATION: ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971. ©Chuan Yang, Wei Zhang, Zhixuan Pang, Jing Zhang, Deling Zou, Xinzhong Zhang, Sicong Guo, Jiye Wan, Ke Wang, Wenyue Pang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 19.01.2021.

Entities:  

Keywords:  COVID-19; Python; Raspberry Pi; auscultation; digital health; ear-contactless; low-cost; phonocardiogram; stethoscope

Year:  2021        PMID: 33436354     DOI: 10.2196/22753

Source DB:  PubMed          Journal:  JMIR Med Inform


  4 in total

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Authors:  Jian Zhu; Chuan-Yang Jiang; Bin Huang; Ji-Min Hu; Si-Zhen Fang; Ke Huang; Yan-Hong Gao; Jiao Yu
Journal:  Infect Drug Resist       Date:  2022-07-07       Impact factor: 4.177

2.  A comparison of the power of breathing sounds signals acquired with a smart stethoscope from a cohort of COVID-19 patients at peak disease, and pre-discharge from the hospital.

Authors:  Nour Kasim; Noa Bachner-Hinenzon; Shay Brikman; Ori Cheshin; Doron Adler; Guy Dori
Journal:  Biomed Signal Process Control       Date:  2022-06-27       Impact factor: 5.076

3.  Automated lung sound analysis using the LungPass platform: a sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19.

Authors:  Elena A Lapteva; Olga N Kharevich; Victoria V Khatsko; Natalia A Voronova; Maksim V Chamko; Irina V Bezruchko; Elena I Katibnikova; Elena I Loban; Mostafa M Mouawie; Helena Binetskaya; Sergey Aleshkevich; Aleksey Karankevich; Vitaly Dubinetski; Jørgen Vestbo; Alexander G Mathioudakis
Journal:  Eur Respir J       Date:  2021-12-02       Impact factor: 16.671

4.  Research on Digital Technology Use in Cardiology: Bibliometric Analysis.

Authors:  Andy Wai Kan Yeung; Stefan Tino Kulnik; Emil D Parvanov; Anna Fassl; Fabian Eibensteiner; Sabine Völkl-Kernstock; Maria Kletecka-Pulker; Rik Crutzen; Johanna Gutenberg; Isabel Höppchen; Josef Niebauer; Jan David Smeddinck; Harald Willschke; Atanas G Atanasov
Journal:  J Med Internet Res       Date:  2022-05-11       Impact factor: 7.076

  4 in total

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