Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope

Rennoll, Valerie; McLane, Ian; Emmanouilidou, Dimitra; West, James E.; Elhilali, Mounya

doi:10.1109/jbhi.2020.3020494

Cited by 25 publications

(16 citation statements)

References 30 publications

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“…Notably, the device includes an integrated external facing microphone that captures simultaneous environmental noise to the lung sound recording and removes the unwanted ambient noises through an adaptive spectral subtraction schema. 24 36 The Feelix Smart Stethoscope also permits onboard data storage with a micro SD card. The device turns on when the user picks up the device or touches the top of the device.…”

Section: Introductionmentioning

confidence: 99%

“…The exclusion of auscultation findings in these guidelines likely stems from its high interobserver variability and subjectivity, regardless of the training level of healthcare providers, and the related difficulty in training healthcare workers to effectively use a conventional stethoscope 18–22. Traditional stethoscopes themselves are also limiting, attenuating higher frequency sounds, like wheezing and crackles, yet transmitting ambient noises and tubular resonance effects 20 23 24. Automated real-time classification of lung sounds or digital auscultation may overcome these limitations 25.…”

Section: Introductionmentioning

confidence: 99%

“…It also eliminates the rubber stethoscope tubing, a source of ambient noise and friction contamination. Notably, the device includes an integrated external facing microphone that captures simultaneous environmental noise to the lung sound recording and removes the unwanted ambient noises through an adaptive spectral subtraction schema 24 36. The Feelix Smart Stethoscope also permits onboard data storage with a micro SD card.…”

Section: Introductionmentioning

confidence: 99%

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Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

et al. 2022

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IntroductionThe WHO’s Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations.Methods and analysisIn a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel’s final interpretation as gold standard.Ethics and disseminationThe study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.Trial registration numberNCT03959956.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

et al. 2022

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“…The exclusion of auscultation findings in these guidelines likely stems from its high inter-observer variability and subjectivity, regardless of the training level of healthcare providers, and the related difficulty in training healthcare workers to effectively use a conventional stethoscope 18-22 . Traditional stethoscopes themselves are also limiting, attenuating higher frequency sounds, like wheezing and crackles, yet transmitting ambient noises and tubular resonance effects 20 23 24 . Automated real-time classification of lung sounds or digital auscultation may overcome these limitations 25 .…”

Section: Introductionmentioning

confidence: 99%

“…It also eliminates the rubber stethoscope tubing, a source of ambient noise and friction contamination. Notably, the device includes an integrated external-facing microphone that captures simultaneous environmental noise to the lung sound recording and removes the unwanted ambient noises through an adaptive spectral subtraction schema 24 36 . The Feelix Smart Stethoscope also permits onboard data storage with a microSD card.…”

Section: Introductionmentioning

confidence: 99%

A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

Ahmed

Mitra

Nair

et al. 2021

Preprint

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IntroductionThe World Health Organisation’s Integrated Management of Childhood Illnesses (IMCI) algorithm relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI performs with high sensitivity but low specificity, leading to over-diagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve pneumonia diagnosis. Our objectives are: (i) assess lung sound recording quality by primary health care workers (HCWs) from under-five children with the Feelix Smart Stethoscope; and (ii) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared to reference paediatrician interpretations.Methods and analysisIn a cross-sectional design, Community HCWs will record lung sounds of ∼1,000 under-five-year-old children with suspected pneumonia at first-level facilities in Zakiganj sub-district, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimize recording quality. Recorded sounds will be assessed against a pre-defined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackle, wheeze, crackle and wheeze, or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications.ConclusionsLung auscultation and reliable interpretation of lung sounds of children are usually not feasible in first-level facilities in Bangladesh and other low- and middle-income countries (LMICs). Incorporating automated lung sound classification within the current IMCI pneumonia diagnostic algorithm may improve childhood pneumonia diagnostic accuracy at LMIC first-level facilities.Ethics and disseminationEthical review has been obtained in Bangladesh (BMRC Registration Number: 09630012018) and in Edinburgh, Scotland, United Kingdom (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.Trial registration numberNCT03959956Article summayStrengths and limitations of this studyEvaluating the quality of lung sound recordings in a first-level facility where auscultation is usually unavailable and challenging to obtain due to a typically crowded and noisy environment and providers may not get enough time to calm the child due to time pressure from a high-volume patient.This study will assess the feasibility of recording lung sounds by front line community health workers who do not usually use conventional stethoscopes during clinical care.Two standardised paediatricians masked to the child’s clinical status will independently classify the recorded lung sounds, and a third masked and independent paediatrician will arbitrate any discrepancies.A machine-learning algorithm developed by Johns Hopkins and Sonavi Labs will detect abnormal lung sounds and be compared with classifications by human listeners/paediatricians.The study will not have chest radiography findings of enrolled children, which is considered by many a gold standard for pneumonia diagnosis, as chest radiography is not available at this level of the health system in Bangladesh. Instead, this study will measure the peripheral oxyhaemoglobin saturation and evaluate clinical examination findings, including respiratory danger signs data.

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Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings

Azam¹,

Ansari²,

Nuhash³

et al. 2022

Artificial Intelligence in Medicine

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Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope

Cited by 25 publications

References 30 publications

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings

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