ArticleBMC medical informatics and decision making2020
Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges.Sensors (Basel, Switzerland) · 2022Pooled it
- An Explainable Fusion of ECG and SpOBioengineering (Basel, Switzerland) · 2025Article
- AI-driven sleep apnea screening with overnight blood oxygen saturation: current practices and future directions.Frontiers in digital health · 2025Review
- [Sleep Innovation].Open respiratory archives · 2024Review
- Article
- Real-Time Obstructive Sleep Apnea Detection from Raw ECG and SpOAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Human YRNA 4 (HY4) plasma levels are a prognostic indicator of SARS-CoV-2 infection clinical severity.microPublication biology · 2023Article
- Real-Time Information Exchange Strategy for Large Data Volumes Based on IoT.Computational intelligence and neuroscience · 2022Article
- Design of an artificial neural network to predict mortality among COVID-19 patients.Informatics in medicine unlocked · 2022Article
- Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review.Yonsei medical journal · 2022Review
- Sleep Monitoring of Children With Nocturnal Enuresis: A Narrative Review.Frontiers in pediatrics · 2021Review
- Sensors, technologies, and classification algorithms for monitoring and diagnosis of sleep apnea.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis.
methodsThis work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time.
resultsOur initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. DISCUSSION: Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising.
conclusionsExperimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.
Indexed as
Identifiers
What OpenQuestion holds
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.