Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Introduction In order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper R...

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Bibliographic Details
Published in:Frontiers in medicine Vol. 10; p. 1269784
Main Authors: Zhang, Pinzhi, Swaminathan, Alagappan, Uddin, Ahmed Abrar
Format: Journal Article
Language:English
Published: Frontiers Media S.A 03-11-2023
Subjects:
artificial intelligence
audio parsing
machine learning
neural networks
predictive analytics
pulmonary diagnostics
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Summary:Introduction In order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis. Methods Our proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM). Results The aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97. Discussion The LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time.
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ISSN:2296-858X
2296-858X
DOI:10.3389/fmed.2023.1269784