R.A.L.E. Lung Sounds 3.0

Owens, Darrell

doi:10.1097/00129191-200307000-00011

Cited by 11 publications

(24 citation statements)

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“…The sensor used for the data collection varied and included an electret microphone and accelerometer in [124], and the Littmann Digital Stethoscope in Littman repository [126]. …”

Section: Resultsmentioning

confidence: 99%

“…47 recordings from an online repository [124] were used. These contained 10 normal, 20 CAS, and 17 DAS recordings.…”

Section: Resultsmentioning

confidence: 99%

“…The data was obtained from an online repository [124]. 13 events, with 4 containing wheeze, 4 containing crackle, and 5 normal were used with a LOOCV technique.…”

Section: Resultsmentioning

confidence: 99%

“…The databases used are from online repositories and from audio CD companion books. The online repositories available were from R.A.L.E [ 124 ], East Tennessee State University repository [ 125 ], Littmann repository [ 126 ], and from SoundCloud [ 127 ]. The audio CDs companion used were from books such as Understanding Lung Sounds 2 nd Edition [ 128 ], Understanding Lung Sounds 3 rd Edition [ 129 ], Auscultation Skills: Breath and Heart Sounds [ 130 ], Fundamentals of Lung and Heart Sounds [ 131 ], Understanding Heart Sounds and Murmurs [ 132 ], Heart and Lung Sounds Reference Library [ 133 ], Secrets Heart & Lung Sounds Workshops [ 134 ], Lung Sounds: An Introduction to the Interpretation of the Auscultatory Finding [ 135 ], and The Chest: Its Signs and Sounds [ 136 ].…”

Section: Resultsmentioning

confidence: 99%

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Automatic adventitious respiratory sound analysis: A systematic review

2017

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BackgroundAutomatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established.ObjectiveTo provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works.Data sourcesA systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification.Study selectionOnly articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated.Data extractionInvestigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved.Data synthesisA total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis.LimitationsDirect comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions.ConclusionA review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventiona...

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“…The sensor used for the data collection varied and included an electret microphone and accelerometer in [124], and the Littmann Digital Stethoscope in Littman repository [126]. …”

Section: Resultsmentioning

confidence: 99%

“…47 recordings from an online repository [124] were used. These contained 10 normal, 20 CAS, and 17 DAS recordings.…”

Section: Resultsmentioning

confidence: 99%

“…The data was obtained from an online repository [124]. 13 events, with 4 containing wheeze, 4 containing crackle, and 5 normal were used with a LOOCV technique.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Automatic adventitious respiratory sound analysis: A systematic review

2017

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“…Moreover, public repositories that have been commonly used in the literature (e.g., R.A.L.E. [ 36 ]) were designed for teaching, typically including a small number of ARS, and usually not containing environmental noise. Therefore, we chose to perform the evaluation on the Respiratory Sound Dataset (RSD), the largest publicly available respiratory sound database, which is described in Section 3.1 .…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?

Rocha

Pessoa

Marques

et al. 2020

Sensors

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(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.

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