ArticleComputational intelligence and neuroscience2022
Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence-Based Granger Panel Model Approach.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 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.
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
6 citing papers in PubMed.
- Retracted: Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence-Based Granger Panel Model Approach.Computational intelligence and neuroscience · 2024Article
- Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers.Scientific reports · 2022Article
- Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease.Frontiers in public health · 2022Article
- Prospects and Challenges of Using Machine Learning for Academic Forecasting.Computational intelligence and neuroscience · 2022Article
- Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy.IEEE open journal of engineering in medicine and biology · 2022Article
Corrections and comments
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep apnea is a serious sleep disorder that occurs when a person's breathing is interrupted during sleep. People with untreated sleep apnea stop breathing repeatedly during their sleep. This study provides an empirical analysis of apnea syndrome using the AI-based Granger panel model approach. Data were collected from the MIT-BIH polysomnographic database (SLPDB). The panel is composed of eighteen patients, while the implementation was done using MATLAB software. The results show that, for the eighteen patients with sleep apnea, there was a significant relationship between ECG-blood pressure (BP), ECG-EEG, and EEG-blood pressure (BP). The study concludes that the long-term interaction between physiological signals can help the physician to understand the risks associated with these interactions. The study would assist physicians to understand the mechanisms underlying obstructive sleep apnea early and also to select the right treatment for the patients by leveraging the potential of artificial intelligence. The researchers were motivated by the need to reduce the morbidity and mortality arising from sleep apnea using AI-enabled technology.
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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.