ArticleSensors (Basel, Switzerland)2020
Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review.Journal of medical systems · 2025Pooled it
- Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- AI-enabled wireless wearable breathing sensor for breathing pattern recognition.Scientific reports · 2026Article
- Towards Wearable Respiration Monitoring: 1D-CRNN-Based Breathing Detection in Smart Textiles.Sensors (Basel, Switzerland) · 2025Article
- Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet.Scientific reports · 2025Article
- Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.Journal of medical Internet research · 2025Article
- Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN.Sensors (Basel, Switzerland) · 2024Article
- Detection of Multiple Respiration Patterns Based on 1D SNN from Continuous Human Breathing Signals and the Range Classification Method for Each Respiration Pattern.Sensors (Basel, Switzerland) · 2023Article
- Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2023Article
- Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor.Frontiers in neuroscience · 2023Article
- Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time.IEEE transactions on biomedical circuits and systems · 2022Article
- Self-Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Applications of deep learning methods in digital biomarker research using noninvasive sensing data.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.
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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.