Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index
Zufei Li, Yanru Li, Guoqiang Zhao, et al.
Sleep And Breathing (2021) Vol. 25, Iss. 4, pp. 2065-2072
Closed Access | Times Cited: 24

Showing 24 citing articles:

A review of automated sleep disorder detection
Shuting Xu, Oliver Faust, Silvia Seoni, et al.
Computers in Biology and Medicine (2022) Vol. 150, pp. 106100-106100
Closed Access | Times Cited: 48
Application of artificial intelligence in the diagnosis of sleep apnea
George Bazoukis, Sandeep Chandra Bollepalli, Cheuk To Chung, et al.
Journal of Clinical Sleep Medicine (2023) Vol. 19, Iss. 7, pp. 1337-1363
Closed Access | Times Cited: 24
Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
Parisa Moridian, Afshin Shoeibi, Marjane Khodatars, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2022) Vol. 12, Iss. 6
Closed Access | Times Cited: 30
Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
Jianming Zhu, Aojie Zhou, Qiong Gong, et al.
Applied Sciences (2022) Vol. 12, Iss. 9, pp. 4218-4218
Open Access | Times Cited: 23
Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpO2 Signals: A Multi-Scale Feature Approach
Nhung Huyen Hoang, Zilu Liang
Sensors (2025) Vol. 25, Iss. 6, pp. 1698-1698
Open Access
The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea
Rafael Rodrigues Dos Santos, M Marumo, Álan Luiz Éckeli, et al.
Frontiers in Cardiovascular Medicine (2025) Vol. 12
Open Access
AI-driven sleep apnea screening with overnight blood oxygen saturation: current practices and future directions
Nhung Huyen Hoang, Zilu Liang
Frontiers in Digital Health (2025) Vol. 7
Open Access
Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea
Malak Abdullah Almarshad, Saad Al-Ahmadi, Md Saiful Islam, et al.
Sensors (2023) Vol. 23, Iss. 18, pp. 7924-7924
Open Access | Times Cited: 9
Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network
Mohsin Ali Tunio, Ashfaque Ahmed Hashmani, Suhail Khokhar, et al.
Engineering Reports (2024)
Open Access | Times Cited: 2
Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio‐feature
Yu‐Ching Hsu, Jung‐Der Wang, Po‐Hsien Huang, et al.
Journal of Sleep Research (2021) Vol. 31, Iss. 2
Closed Access | Times Cited: 12
Detection of pediatric obstructive sleep apnea using a multilayer perceptron model based on single-channel oxygen saturation or clinical features
Yunxiao Wu, Yifan Jia, Xiaolin Ning, et al.
Methods (2022) Vol. 204, pp. 361-367
Closed Access | Times Cited: 8
A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population
Pona Park, Jeong‐Whun Kim
Journal of Korean Medical Science (2023) Vol. 38, Iss. 7
Open Access | Times Cited: 4
Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach
Praveen K. Tyagi, Dheeraj Agarwal
Biomedical Engineering Letters (2023) Vol. 13, Iss. 3, pp. 293-312
Open Access | Times Cited: 2
Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis
Xiang Liu, Yongqiang Shu, Pengfei Yu, et al.
Frontiers in Neurology (2022) Vol. 13
Open Access | Times Cited: 4
Automatic Obstructive Sleep Apnea Identification Using First Order Statistics Features of Electrocardiogram and Machine Learning
Aida Noor Indrawati, Nuryani Nuryani, Wiharto Wiharto, et al.
Lecture notes in electrical engineering (2024), pp. 151-165
Closed Access
Developing Probabilistic Ensemble Machine Learning Models for Home-Based Sleep Apnea Screening using Overnight SpO2 Data at Varying Data Granularity
Zilu Liang
Research Square (Research Square) (2024)
Open Access
Developing Probabilistic Ensemble Machine Learning Models for Home-Based Sleep Apnea Screening using Overnight SpO2 Data at Varying Data Granularity
Zilu Liang
Research Square (Research Square) (2024)
Open Access
Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity
Zilu Liang
Sleep And Breathing (2024) Vol. 28, Iss. 6, pp. 2409-2420
Closed Access
Artificial Intelligence for Diagnosis of Obstructive Sleep Apnea
Jasmine L. May, Roneil G. Malkani
Current Pulmonology Reports (2024)
Closed Access
Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques
Moises Ramos-Martinez, Felipe D. J. Sorcia‐Vázquez, Gerardo Ortíz-Torres, et al.
Algorithms (2024) Vol. 17, Iss. 11, pp. 527-527
Open Access
AI-driven approaches for automatic detection of sleep apnea/hypopnea based on human physiological signals: a review
Dandan Peng, Le Sun, Qian Zhou, et al.
Health Information Science and Systems (2024) Vol. 13, Iss. 1
Closed Access
Systematic Review of Diagnostic Modalities for Sleep Apnea: A Comprehensive Analysis
P Bhavitha, J Harish, Ravi Raja A, et al.
(2023)
Closed Access | Times Cited: 1
Sleep disorder and apnea events detection framework with high performance using two-tier learning model design
Recep Sinan Arslan
PeerJ Computer Science (2023) Vol. 9, pp. e1554-e1554
Open Access | Times Cited: 1
Sleep Apnea Prediction in the Post-Stroke Patient based on the Sleep, Pain and Depression Parameters
Haslaile Abdullah, Siti Zura A. Jalil, Norliza Mohd Noor, et al.
(2023), pp. 138-141
Closed Access | Times Cited: 1
Page 1

Scroll to top