ArticleScientific reports2019
A Novel Method for Automatic Identification of Breathing State.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- Classification of Breathing Phase and Path with In-Ear Microphones.Sensors (Basel, Switzerland) · 2024Article
- Review
- A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis.Sensors (Basel, Switzerland) · 2023Article
- Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2023Review
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges.Frontiers in neurology · 2023Review
- Sound-guided assessment and localization of pulmonary air leak.Bioengineering & translational medicine · 2023Article
- Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.Heliyon · 2022Article
- Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.