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:

Frequency Network Analysis of Heart Rate Variability for Obstructive Apnea Patient Detection
Dong Zhao, Xiang Li, Wei Chen
IEEE Journal of Biomedical and Health Informatics (2017) Vol. 22, Iss. 6, pp. 1895-1905
Closed Access | Times Cited: 24

Showing 24 citing articles:

Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea
Daniele Padovano, Arturo Martínez‐Rodrigo, J.M. Pastor, et al.
Applied Sciences (2025) Vol. 15, Iss. 1, pp. 433-433
Open Access | Times Cited: 1
A RR interval based automated apnea detection approach using residual network
Lei Wang, Youfang Lin, Jing Wang
Computer Methods and Programs in Biomedicine (2019) Vol. 176, pp. 93-104
Closed Access | Times Cited: 63
Accurate detection of sleep apnea with long short-term memory network based on RR interval signals
Oliver Faust, Ragab Barika, Alex Shenfield, et al.
Knowledge-Based Systems (2020) Vol. 212, pp. 106591-106591
Open Access | Times Cited: 60
Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals
Asghar Zarei, Babak Mohammadzadeh Asl
Biomedical Signal Processing and Control (2020) Vol. 59, pp. 101927-101927
Closed Access | Times Cited: 56
A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals
Md Belal Bin Heyat, Faijan Akhtar, Asif Khan, et al.
Applied Sciences (2020) Vol. 10, Iss. 21, pp. 7410-7410
Open Access | Times Cited: 53
Semi-Supervised Learning for Low-Cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram
Shuaicong Hu, Yanan Wang, Jian Liu, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 27, Iss. 11, pp. 5281-5292
Closed Access | Times Cited: 19
A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications
E. Smily JeyaJothi, J. Anitha, Shalli Rani, et al.
BioMed Research International (2022) Vol. 2022, pp. 1-21
Open Access | Times Cited: 24
Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT
Shiliang Shao, Guangjie Han, Ting Wang, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 12, pp. 5841-5850
Closed Access | Times Cited: 23
A residual deep learning framework for sleep apnea diagnosis from single lead electrocardiogram signals: An explainable artificial intelligence approach
Biswarup Ganguly, Rajdeep Dasgupta, Debangshu Dey
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110481-110481
Closed Access
Phyjama
Ali Kiaghadi, S. Zohreh Homayounfar, Jeremy Gummeson, et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2019) Vol. 3, Iss. 3, pp. 1-29
Closed Access | Times Cited: 35
A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals
Chenglu Sun, Chen Chen, Jiahao Fan, et al.
Journal of Neural Engineering (2019) Vol. 16, Iss. 6, pp. 066020-066020
Closed Access | Times Cited: 24
Computational ensemble expert system classification for the recognition of bruxism using physiological signals
Pragati Tripathi, M.A. Ansari, Tapan Kumar Gandhi, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e25958-e25958
Open Access | Times Cited: 2
Performance Comparison for Ballistocardiogram Peak Detection Methods
Ahmad Suliman, Charles Carlson, Carl J. Ade, et al.
IEEE Access (2019) Vol. 7, pp. 53945-53955
Open Access | Times Cited: 22
On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning
Daniele Padovano, Arturo Martínez‐Rodrigo, J.M. Pastor, et al.
IEEE Access (2022) Vol. 10, pp. 92710-92725
Open Access | Times Cited: 12
Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling
Maryam Faal, Farshad Almasganj
Biomedical Signal Processing and Control (2021) Vol. 68, pp. 102685-102685
Open Access | Times Cited: 15
Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging
Ahmad Suliman, Md Rakibul Mowla, Alaleh Alivar, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2693-2693
Open Access | Times Cited: 4
Evaluation of the PSO Metaheuristic Algorithm in Different Types of Sleep Apnea Diagnosis Using RR Intervals
Zeinab Kohzadi, Reza Safdari, Khosro Sadeghniiat‐Haghighi
Journal of Biomedical Physics and Engineering (2023) Vol. 13, Iss. 2
Open Access | Times Cited: 3
Age-related network topological difference based on the sleep ECG signal
Guohun Zhu, Cong Wang, Feng Liu, et al.
Physiological Measurement (2018) Vol. 39, Iss. 8, pp. 084009-084009
Closed Access | Times Cited: 8
Individual-specific networks for prediction modelling – A scoping review of methods
Mariella Gregorich, Federico Melograna, Martina Sunqvist, et al.
BMC Medical Research Methodology (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 5
Performance Evaluation of Processing Methods for Ballistocardiogram Peak Detection
Ahmad Suliman, Charles Carlson, Steve Warren, et al.
(2018), pp. 502-505
Closed Access | Times Cited: 6
An improved time-frequency representation aided deep learning framework for automated diagnosis of sleep apnea from ECG signals
Biswarup Ganguly, Debangshu Dey
Measurement (2024) Vol. 242, pp. 116170-116170
Closed Access
Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge
Daniele Padovano, Arturo Martínez‐Rodrigo, J.M. Pastor, et al.
Computing in cardiology (2020)
Open Access | Times Cited: 2
Various Methods for Identification of Obstructive Sleep Apnea Using Electrocardiogram Features
Remalli Rohan, D. Santhosh Kumar, Srinivasa Rao Patri
Journal of scientific research (2020) Vol. 64, Iss. 01, pp. 169-277
Open Access | Times Cited: 1
A New Method for Electrocardiogram Features Extraction Using Slope Change Coefficients
Skander Bensegueni
International Journal of Electronics and Telecommunications (2022), pp. 33-39
Open Access
Page 1

Scroll to top