ArticleBioinformatics (Oxford, England)2018
Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Acoustic Lung Imaging Utilized in Continual Assessment of Patients with Obstructed Airway: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals.Bioinformatics advances · 2024Article
- Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube.Scientific reports · 2023Article
- A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis.Sensors (Basel, Switzerland) · 2023Article
- A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges.Frontiers in neurology · 2023Review
- Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.Heliyon · 2022Article
- Determining airflow obstruction from tracheal sound analysis: simulated tests and evaluations in patients with acromegaly.Medical & biological engineering & computing · 2022Article
- Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients.BMC medical informatics and decision making · 2021Article
- Use of carbon nanotube sensor for detecting postoperative abnormal respiratory waveforms.Journal of thoracic disease · 2021Article
- Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System.Sensors (Basel, Switzerland) · 2021Article
- Does Facemask Impact Diagnostic During Pulmonary Auscultation?IFAC-PapersOnLine · 2021Article
- A Phlegm Stagnation Monitoring Based on VDS Algorithm.Journal of healthcare engineering · 2020Article
- A Novel Method for Automatic Identification of Breathing State.Scientific reports · 2019Article
- A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network.International journal of biological sciences · 2019Article
- Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review.Entropy (Basel, Switzerland) · 2018Review
- Demonstrating the Potential of Using Transcutaneous Oxygen and Carbon Dioxide Tensions to Assess the Risk of Pressure Injuries.International journal of biological sciences · 2018Article
- Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.International journal of biological sciences · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results: We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation: The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact: yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information: Supplementary data are available at Bioinformatics online.
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