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A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study.
Yang, Chuan; Zhang, Wei; Pang, Zhixuan; Zhang, Jing; Zou, Deling; Zhang, Xinzhong; Guo, Sicong; Wan, Jiye; Wang, Ke; Pang, Wenyue.
  • Yang C; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Zhang W; Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
  • Pang Z; Sewickley Academy Senior High School, Pittsburgh, PA, United States.
  • Zhang J; School of Population Health, University of New South Wales, Sydney, Australia.
  • Zou D; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Zhang X; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Guo S; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Wan J; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Wang K; Department of Cardiac Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
  • Pang W; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China.
JMIR Med Inform ; 9(1): e22753, 2021 Jan 19.
Article in English | MEDLINE | ID: covidwho-1034894
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials Language: English Journal: JMIR Med Inform Year: 2021 Document Type: Article Affiliation country: 22753

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials Language: English Journal: JMIR Med Inform Year: 2021 Document Type: Article Affiliation country: 22753