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:

Radar-based sleep stage classification in children undergoing polysomnography: a pilot-study
Robbin de Goederen, Suan Hui Pu, Maria Silos Viu, et al.
Sleep Medicine (2021) Vol. 82, pp. 1-8
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation
Xinyu Huang, Kimiaki Shirahama, Muhammad Tausif Irshad, et al.
Sensors (2023) Vol. 23, Iss. 7, pp. 3446-3446
Open Access | Times Cited: 17

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video
Muhammad Awais, Xi Long, Bin Yin, et al.
IEEE Journal of Biomedical and Health Informatics (2021) Vol. 25, Iss. 5, pp. 1441-1449
Open Access | Times Cited: 40

Unobtrusive Bed Monitor State of the Art
T. Tamura, Ming Huang
Sensors (2025) Vol. 25, Iss. 6, pp. 1879-1879
Open Access

ECG-grained Cardiac Monitoring Using UWB Signals
Zhi Wang, Beihong Jin, Siheng Li, et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2022) Vol. 6, Iss. 4, pp. 1-25
Open Access | Times Cited: 17

The past, present, and future of sleep quality assessment and monitoring
Yanyan Chen, Enyuan Zhou, Yu Wang, et al.
Brain Research (2023) Vol. 1810, pp. 148333-148333
Closed Access | Times Cited: 9

Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning
Xinyu Huang, Franziska Schmelter, Muhammad Tausif Irshad, et al.
Computers in Biology and Medicine (2023) Vol. 166, pp. 107501-107501
Open Access | Times Cited: 9

Non-contact Sleep/Wake Monitoring Using Impulse-Radio Ultrawideband Radar in Neonates
Won Hyuk Lee, Seung Hyun Kim, Jae Yoon Na, et al.
Frontiers in Pediatrics (2021) Vol. 9
Open Access | Times Cited: 21

Robust Radar-Based Vital Sensing With Adaptive Sinc Filtering and Random Body Motion Rejections
Souvik Hazra, Alessandra Fusco, Gamze Naz Kiprit, et al.
IEEE Sensors Letters (2023) Vol. 7, Iss. 5, pp. 1-4
Closed Access | Times Cited: 8

Non-contact determination of sleep/wake state in residential environments by neural network learning of microwave radar and electroencephalogram–electrooculogram measurements
Xiaorui Wang, Daisuke Matsushita
Building and Environment (2023) Vol. 233, pp. 110095-110095
Closed Access | Times Cited: 6

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls
Pranavan Somaskandhan, Timo Leppänen, Philip I. Terrill, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 6

Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar
Emad Arasteh, Eline R. de Groot, Demi van den Ende, et al.
Frontiers in Sleep (2023) Vol. 2
Open Access | Times Cited: 6

Recognition of Sleep-Wake Stages by Deep Takagi-Sugeno-Kang Fuzzy Classifier with Random Rule Heritage
Zhou Ta, Guanjin Wang, Kup‐Sze Choi, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2023) Vol. 7, Iss. 5, pp. 1458-1469
Closed Access | Times Cited: 5

Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
Youngkeun Yoo, Chaewon Jung, Hyun‐Chool Shin
Applied Sciences (2023) Vol. 13, Iss. 7, pp. 4468-4468
Open Access | Times Cited: 5

Toward Ant-Sized Moving Object Localization Using Deep Learning in FMCW Radar: A Pilot Study
Nakorn Kumchaiseemak, Itthi Chatnuntawech, Surat Teerapittayanon, et al.
IEEE Transactions on Geoscience and Remote Sensing (2022) Vol. 60, pp. 1-10
Closed Access | Times Cited: 8

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach
Emad Arasteh, Esther S. Veldhoen, Xi Long, et al.
Sensors (2023) Vol. 23, Iss. 18, pp. 7665-7665
Open Access | Times Cited: 4

Evaluation of automated pediatric sleep stage classification using U-Sleep - a convolutional neural network
Ajay Kevat, Rylan Steinkey, Sadasivam Suresh, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1

Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency‐modulated continuous‐wave radar
Ji-Hyun Lee, Hyunwoo Nam, Dong Hyun Kim, et al.
Journal of Sleep Research (2023) Vol. 33, Iss. 1
Open Access | Times Cited: 3

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

Development of a non-contact sleep monitoring system for children
Masamitsu Kamon, Shima Okada, Masafumi Furuta, et al.
Frontiers in Digital Health (2022) Vol. 4
Open Access | Times Cited: 5

Sleep stage estimation method based on state transition using millimeter-wave radar
Yi Lü, Zhaocheng Yang, Jianhua Zhou
(2024), pp. 85-85
Closed Access

Noncontact respiration monitoring techniques in young children: A scoping review
Marjolein van der Linden­­, Esther S. Veldhoen, Emad Arasteh, et al.
Pediatric Pulmonology (2024) Vol. 59, Iss. 7, pp. 1871-1884
Open Access

LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis
Ruijie Zhang, Xin Zheng, Lu Zhang, et al.
Journal of Neuroscience Methods (2024) Vol. 410, pp. 110222-110222
Closed Access

Page 1 - Next Page

Scroll to top