Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments

Emmanouilidou, Dimitra; McCollum, Eric D.; Park, Daniel E.; Elhilali, Mounya

doi:10.1109/tbme.2017.2717280

Cited by 68 publications

(45 citation statements)

References 37 publications

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“…Other sound assessment Several recent audio processing methods have been proposed regarding non-cry signals and concerning either pre-linguistic vocalizations (including cooing) (Fuller andHorii, 1986, 1988;Pokorny et al, 2016Pokorny et al, , 2018. Non-voice analyses were also proposed in different contexts such as external noise detection (Raboshchuk et al, 2018a,b), EEG sonification (Gomez et al, 2018) or lung sound assessment (Emmanouilidou et al, 2017).…”

Section: Automatic Cry Segmentationmentioning

confidence: 99%

“…It was shown that sonification methods perform similarly well, with a smaller inter-observer variability in comparison with visual interpretation. Lung audio recordings of 1000 children were also studied (Emmanouilidou et al, 2017). First, noise suppression techniques were applied to discard ambient sounds, sensors artifacts or crying.…”

Section: Automatic Cry Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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Objective: Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). Approach: For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts—full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. Main results: The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. Significance: Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.

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Section: Automatic Cry Segmentationmentioning

confidence: 99%

Section: Automatic Cry Segmentationmentioning

confidence: 99%

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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show abstract

“…14 We have also developed and internally validated a fully automated lung sound processing algorithm that can identify abnormal lung sounds from PERCH recordings with nearly 90% accuracy. 13 In this research, we extend this initial body of work to show that human interpretation of digital lung recordings has important clinical relationships with radiographic pneumonia and pneumonia mortality. While these results are encouraging it is important to stress that they should be considered as only an initial step towards clinical or research application given the lack of a gold standard for pneumonia diagnosis and the inherent limitations of the WHO-defined radiographic pneumonia methodology, as discussed below.…”

Section: Digitally Recorded Lung Sounds and Mortalitymentioning

confidence: 80%

Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children

McCollum

Park

Watson

et al. 2020

Pediatric Pulmonology

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Background: Whether digitally recorded lung sounds are associated with radiographic pneumonia or clinical outcomes among children in low-income and middleincome countries is unknown. We sought to address these knowledge gaps. Methods: We enrolled 1 to 59 month old children hospitalized with pneumonia at eight African and Asian Pneumonia Etiology Research for Child Health sites in six countries, recorded digital stethoscope lung sounds, obtained chest radiographs, and collected clinical outcomes. Recordings were processed and classified into

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“…On ICBHI17 dataset, an accuracy of 52.26% was reported. Emmanouilidou et al [ 11 ] proposed a robust approach to identify lung sounds in the presence of noise. In their experiments, with 1K+ volunteers (over 250 hours of data), an accuracy of 86.7% was reported.…”

Section: Introductionmentioning

confidence: 99%

Automatic Lung Health Screening Using Respiratory Sounds

et al. 2021

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Significant changes have been made on audio-based technologies over years in several different fields. Healthcare is no exception. One of such avenues is health screening based on respiratory sounds. In this paper, we developed a tool to detect respiratory sounds that come from respiratory infection carrying patients. Linear Predictive Cepstral Coefficient (LPCC)-based features were used to characterize such audio clips. With Multilayer Perceptron (MLP)-based classifier, in our experiment, we achieved the highest possible accuracy of 99.22% that was tested on a publicly available respiratory sounds dataset (ICBHI17) (Rocha et al. Physiol. Meas. 40(3):035,001 2019) of size 6800+ clips. In addition to other popular machine learning classifiers, our results outperformed common works that exist in the literature.

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Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments

Abstract: Unlike existing methodologies in the literature, the proposed work is not limited in scope or confined to laboratory settings: This work validates a practical method for fully automated chest sound processing applicable to realistic and noisy auscultation settings.

Cited by 68 publications

References 37 publications

Video and audio processing in paediatrics: a review

Video and audio processing in paediatrics: a review

Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children

Automatic Lung Health Screening Using Respiratory Sounds

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