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OpenAlex Citation Counts

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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:

EEG sleep stages identification based on weighted undirected complex networks
Mohammed Diykh, Yan Li, Shahab Abdulla
Computer Methods and Programs in Biomedicine (2019) Vol. 184, pp. 105116-105116
Closed Access | Times Cited: 58

Showing 26-50 of 58 citing articles:

EEG-Based Emotion Recognition by Exploiting Fused Network Entropy Measures of Complex Networks across Subjects
Longxin Yao, Mingjiang Wang, Yun Lu, et al.
Entropy (2021) Vol. 23, Iss. 8, pp. 984-984
Open Access | Times Cited: 20
Dynamics of sleep: Exploring critical transitions and early warning signals
Susanne M. M. de Mooij, Tessa F. Blanken, Raoul P. P. P. Grasman, et al.
Computer Methods and Programs in Biomedicine (2020) Vol. 193, pp. 105448-105448
Open Access | Times Cited: 18
Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection
Hanan Al‐Hadeethi, Shahab Abdulla, Mohammed Diykh, et al.
Diagnostics (2021) Vol. 12, Iss. 1, pp. 74-74
Open Access | Times Cited: 17
Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages
Ahsan Habib, Mohammod Abdul Motin, Thomas Penzel, et al.
IEEE Transactions on Biomedical Engineering (2022) Vol. 70, Iss. 6, pp. 1717-1728
Closed Access | Times Cited: 12
Automated Sleep Staging Classification System Based On Convolutional Neural Network Using Polysomnography Signals
Santosh Kumar Satapathy, Shrinibas Pattnaik, Rama Krushna Rath
2022 IEEE Delhi Section Conference (DELCON) (2022), pp. 1-10
Closed Access | Times Cited: 9
Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification
Ignacio Alonso Zapata, Yan Li, Peng Wen
IEEE Access (2022) Vol. 10, pp. 71299-71310
Open Access | Times Cited: 8
Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model
Ning Shen, Tian Luo, Chen Chen, et al.
Journal of Neural Engineering (2022) Vol. 19, Iss. 5, pp. 056009-056009
Closed Access | Times Cited: 8
A Deep Neural Model CNN-LSTM Network for Automated Sleep Staging Based on a Single-Channel EEG Signal
Santosh Kumar Satapathy, Khelan Shah, Shrey Shah, et al.
Lecture notes in networks and systems (2023), pp. 55-71
Closed Access | Times Cited: 4
Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features
Tianning Li, Yi Huang, Peng Wen, et al.
Brain Informatics (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Machine Learning Based Hardware Architecture for DOA Measurement From Mice EEG
Mehdi Hasan Chowdhury, Abdelrahman B.M. Eldaly, Stephen Kugbere Agadagba, et al.
IEEE Transactions on Biomedical Engineering (2021) Vol. 69, Iss. 1, pp. 314-324
Closed Access | Times Cited: 10
Investigating the use of uni-directional and bi-directional long short-term memory models for automatic sleep stage scoring
Luay Fraiwan, Mohanad Alkhodari
Informatics in Medicine Unlocked (2020) Vol. 20, pp. 100370-100370
Open Access | Times Cited: 10
Real-Time Excitation of Slow Oscillations during Deep Sleep Using Acoustic Stimulation
Marek Piorecký, Vlastimil Koudelka, Václava Piorecká, et al.
Sensors (2021) Vol. 21, Iss. 15, pp. 5169-5169
Open Access | Times Cited: 9
A comprehensive evaluation of contemporary methods used for automatic sleep staging
Debanjan Sarkar, Dwaipayan Guha, Puspal Tarafdar, et al.
Biomedical Signal Processing and Control (2022) Vol. 77, pp. 103819-103819
Closed Access | Times Cited: 5
Studying the Effects of Compression in EEG-Based Wearable Sleep Monitoring Systems
Deland Hu Liu, Syed Anas Imtiaz
IEEE Access (2020) Vol. 8, pp. 168486-168501
Open Access | Times Cited: 6
Investigation of automatic spindle detection in sleep EEG signals contaminated with noise and artifact sources
Ahmed S. Eltrass, Noha H. Ghanem
Journal of Ambient Intelligence and Humanized Computing (2022) Vol. 14, Iss. 9, pp. 12725-12746
Open Access | Times Cited: 4
Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine
Akhand Rai, Vishnu Priya Kapu, P. S. Balaji, et al.
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering (2024) Vol. 238, Iss. 6, pp. 1135-1152
Closed Access
Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis
Haoyu Wang, Xihe Qiu, Bin Li, et al.
Complex & Intelligent Systems (2024) Vol. 11, Iss. 1
Open Access
Eeg-Based Sleep Stage Classification Using Cnn with Squeeze-and-Excitation Blocks in a Short-Visual Geometric Group
Ignacio Alonso Zapata, Yan Li, Peng Wen
(2024)
Closed Access
Unraveling sleep patterns: Supervised contrastive learning with self-attention for sleep stage classification
Chandra Bhushan Kumar, Arnab Kumar Mondal, Manvir Bhatia, et al.
Applied Soft Computing (2024) Vol. 167, pp. 112298-112298
Closed Access
An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis
Hanan Al‐Hadeethi, Shahab Abdulla, Mohammed Diykh, et al.
Frontiers in Neuroinformatics (2022) Vol. 15
Open Access | Times Cited: 2
Sleep Staging Framework with Physiologically Harmonized Sub-Networks
Zheng Chen, Ziwei Yang, Dong Wang, et al.
Methods (2022) Vol. 209, pp. 18-28
Open Access | Times Cited: 2
Supervised Classification with Short-Term Memory of Sleep Stages using Cardio-respiratory and Body Movement Variables
Asma Gasmi, Vincent Augusto, Paul-Antoine Beaudet, et al.
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (2021), pp. 1083-1088
Open Access | Times Cited: 1
Automated Classification of Sleep Stages Based on Electroencephalogram Signal Using Machine Learning Techniques
Santosh Kumar Satapathy, D. Loganathan, M. V. Sangameswar, et al.
Algorithms for intelligent systems (2021), pp. 407-416
Closed Access | Times Cited: 1
Sleep staging prediction model based on XGBoost
Siyuan Liu, Liu Jingyuan, Gu Hangping, et al.
2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) (2021)
Closed Access | Times Cited: 1
A Machine Learning Model for Automatic Sleep Staging Based on Single-Channel EEG Signals
Santosh Kumar Satapathy, Hari Kishan Kondaveeti, A. S. Venkata Praneel
Lecture notes in networks and systems (2023), pp. 193-212
Closed Access

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