ArticleInternational journal of biological sciences2018
Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.
Article in International journal of biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Real-time airway monitoring system using binary classification model based on respiratory sounds of rabbits with a tracheostomy tube.Scientific reports · 2025Article
- Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube.Scientific reports · 2023Article
- Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.Heliyon · 2022Article
- The mechanical ventilator of the future: a breath of hope for the viral pandemics to come.The Pan African medical journal · 2022Article
- Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children.Frontiers in pediatrics · 2021Article
- Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review.Entropy (Basel, Switzerland) · 2018Review
- Special issue on Computational Resources and Methods in Biological Sciences.International journal of biological sciences · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.
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Registered trials
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