ArticleSensors (Basel, Switzerland)2020
Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network.
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 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis.PloS one · 2022Pooled it
- Machine learning-based prediction of ventilator therapeutic pressure for optimized CPAP titration.BMC medical informatics and decision making · 2026Article
- Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG.Frontiers in artificial intelligence · 2026Article
- Towards Wearable Respiration Monitoring: 1D-CRNN-Based Breathing Detection in Smart Textiles.Sensors (Basel, Switzerland) · 2025Article
- Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.Journal of medical Internet research · 2025Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- [Sleep Innovation].Open respiratory archives · 2024Review
- CPAP Treatment at Home after Acute Decompensated Heart Failure in Patients with Obstructive Sleep Apnea.Journal of clinical medicine · 2024Article
- A comparative study of accuracy in major adaptive filters for motion artifact removal in sleep apnea tests.Medical & biological engineering & computing · 2024Article
- Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.Heliyon · 2022Article
- Screening Severe Obstructive Sleep Apnea in Children with Snoring.Diagnostics (Basel, Switzerland) · 2021Article
- Smart Sensors for Healthcare and Medical Applications.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.
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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.