Acoustics based assessment of respiratory diseases using GMM classification

Mayorga, P.; Druzgalski, C.; Morelos, Raúl Ludwig; González, O. H.; Vidales, J.

doi:10.1109/iembs.2010.5628092

Cited by 58 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When results are compared with other approaches [12,28], it was noticeable that classification rates, for supervised proposal, were closer to these results. Table 4 Results from normal signals were poor in the unsupervised training.…”

Section: Discussionmentioning

confidence: 86%

Artificial Neural Networks for Acoustic Lung Signals Classification

Orjuela-Cañón

Gómez-Cajas

Moreno

2014

Advanced Information Systems Engineering

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 86%

Artificial Neural Networks for Acoustic Lung Signals Classification

Orjuela-Cañón

Gómez-Cajas

Moreno

2014

Advanced Information Systems Engineering

View full text Add to dashboard Cite

“…Most respiratory sounds investigated thus far were breathing sounds or lung sounds rather than cough sounds. Second, the GMM-UBM technique has proven its efficiency in respiratory sound classification [39][40][41][42], and has been found to perform reliably, even with small data samples. While some recent studies showed that deep learning approaches such as Convolutional Neural Networks are more efficient for respiratory sound classification (notably lung sounds), these algorithms require a large dataset [43][44][45][46][47].…”

Section: Discussionmentioning

confidence: 99%

Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

et al. 2019

View full text Add to dashboard Cite

(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children’s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts—healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model–Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians’ labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from ‘healthy’ children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.

show abstract

“…By experimenting on a public dataset, named R.A.L.E. [9], we show that the detection model performs better than previous works [12,17,20] which use hand-crafted acoustic feature based machine learning model, applied on the same dataset (R.A.L.E. [9].…”

Section: Introductionmentioning

confidence: 96%

WheezeD: Respiration Phase Based Wheeze Detection Using Acoustic Data From Pulmonary Patients Under Attack

Chatterjee¹,

Rahman²,

Nemanti³

et al. 2019

Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters

View full text Add to dashboard Cite

Wheezing is one of the most prominent symptoms for pulmonary attack. Hence, wheezing detection has attracted a lot of attention in recent years. However, there is a dearth of a reliable method that can automatically detect wheezing events during each respiration phase in presence of several concurrent sounds such as cough, throat clearing, and nasal breathing. In this paper, we develop a model called WheezeD which, to the best of our knowledge, represents the first step towards developing a computational model for respiration phased based wheeze detection. WheezeD has two components, first, we d evelop a n a lgorithm t o d etect respiration p hase from audio data. We, then transform the audio into 2-D spectro-temporal image and develop a convolutional neural network (CNN) based wheeze detection model. We evaluate the model performance and compare them to conventional approaches. Experiments on a public dataset show that our model can identify wheezing event with an accuracy of 96.99%, specificity of 97.96%, and sensitivity of 96.08%, which is over 10% improvement in performance compared to the best accuracy reported in the literature by using traditional machine learning models on the same dataset. Moreover, we discuss how WheezeD may be used towards assessment and computation of patient severity.

show abstract

Acoustics based assessment of respiratory diseases using GMM classification

Cited by 58 publications

References 7 publications

Artificial Neural Networks for Acoustic Lung Signals Classification

Artificial Neural Networks for Acoustic Lung Signals Classification

Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

WheezeD: Respiration Phase Based Wheeze Detection Using Acoustic Data From Pulmonary Patients Under Attack

Contact Info

Product

Resources

About