Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

Sosa, Germán D.; Cruz-Roa, Ángel; González, Fabio A.

doi:10.1117/12.2073614

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…This maximal margin classifier aims to find the hyperplane in an N-dimensional space that distinctly classifies the data points [92]. [14,37,59,63,65,66,78,87,[93][94][95][96][97][98][99] k-NN This classifier intends to classify a set of unlabeled data by assigning it to the class that contains the most similar labeled data points [100]. [14,39,59,63,65,98,99] DT This technique classifies data by posing questions regarding the item's features.…”

Section: Svmmentioning

confidence: 99%

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

Garcia-Mendez,

Lal,

Herasevich

et al. 2023

Bioengineering

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Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

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Section: Svmmentioning

confidence: 99%

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

Garcia-Mendez,

Lal,

Herasevich

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…A series of experiments were performed using this novel dataset and performance was compared with the BodyBeat [6] system. Several other shallow and deep learning based pulmonary activity detection works like wheeze detection [7][8][9][10][10][11][12][13][14][15] and cough detection [16][17][18] exist in literature. However, they often use limited training data which is not collected with a commodity smartphone.…”

Section: Introductionmentioning

confidence: 99%

DeepLung: Smartphone Convolutional Neural Network-Based Inference of Lung Anomalies for Pulmonary Patients

2019

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DeepLung is an end-to-end deep learning based audio sensing and classification framework for lung anomaly (e.g. cough, wheeze) detection for pulmonary patients from streaming audio and inertial sensor data from a chest-held smartphone. We design and develop 1-D and 2-D convolutional neural networks for DeepLung, and train them using the Interspeech 2010 Paralinguistic Challenge features. Two different audio windowing schemes: i) real-time respiration cycle based natural windowing, and ii) static length windowing are compared and experimented with. Classifiers are developed considering 2 different system architectures: i) mobile-cloud hybrid architecture, and ii) mobile in-situ architecture. Patient privacy is preserved in the phone by filtering speech with a shallow classifier. To evaluate DeepLung, a novel and rigorous lung activity dataset is made by collecting audio and inertial sensor data from more than 131 real pulmonary patients and healthy subjects and annotated accurately by professional crowdsourcing. Experimental results show that the best combination of DeepLung convolutional neural network is 15-27% more accurate when compared to a stateof-the-art smartphone based body sound detection system, with a best F1 score of 98%.

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FPGA implementation of an automatic wheezing detection system

Bahoura

2018

Biomedical Signal Processing and Control

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Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

Cited by 5 publications

References 1 publication

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

DeepLung: Smartphone Convolutional Neural Network-Based Inference of Lung Anomalies for Pulmonary Patients

FPGA implementation of an automatic wheezing detection system

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