Publications citing You Snooze You Win: The PhysioNet/Computing in Cardiology Challenge 2018 v1.0.0

Brain and brain-heart Granger causality during wakefulness and sleep
Helmi Abdalbari, Mohammad Durrani, Shivam Pancholi et al. Frontiers in Neuroscience, 2022. Cites: paper

Comparison of deep transfer learning algorithms and transferability measures for wearable sleep staging
Samuel H. Waters, Gari D. Clifford. BioMedical Engineering OnLine, 2022. Cites: paper

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks
Caterina Fuster-Barceló, Pedro Peris Lopez, Carmen Cámara. Neurocomputing, 2022. Cites: paper

A systematic review of deep learning methods for modeling electrocardiograms during sleep
Chenxi Sun, Shenda Hong, Jingyu Wang et al. Physiological Measurement, 2022. Cites: paper

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassilis Kilintzis et al. JMIR Medical Informatics, 2022. Cites: paper

Automatic sleep scoring with LSTM networks: impact of time granularity and input signals
Alexandra-Maria Tăuţan, Alessandro C. Rossi, Bogdan Emanuel Ionescu. Biomedizinische Technik/Biomedical Engineering, 2022. Cites: paper

Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art Deep Learning Methods
Huy Phan, Alfred Mertins, Mathias Baumert. IEEE Transactions on Biomedical Engineering, 2022. Cites: paper

Feature Imitating Networks
Sari Saba-Sadiya, Tuka Alhanai, Mohammad M. Ghassemi. IEEE International Conference on Acoustics Speech and Signal Processing, 2022. Cites: paper

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)
Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassiis Kilintzis et al. 2022. Cites: paper

How Machine Learning is Powering Neuroimaging to Improve Brain Health
Nalini Singh, Jordan B. Harrod, Sandya Subramanian et al. Neuroinformatics, 2022. Cites: paper

Automatic sleep staging of EEG signals: recent development, challenges, and future directions
Huy Phan, Kaare B. Mikkelsen. Physiological Measurement, 2022. Cites: paper

Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review
Zawar Hussain, Quan Z. Sheng, Wei Emma Zhang et al. ACM Transactions on Computing for Healthcare, 2022. Cites: paper

DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning
Róbert Fónod. AI, 2022. Cites: paper

Fractal nature of arterial blood oxygen saturation data
Gennady P. Chuiko, Yevhen S. Darnapuk. RADIOELECTRONIC AND COMPUTER SYSTEMS, 2022. Cites: paper

Robust learning from corrupted EEG with dynamic spatial filtering
Hubert J. Banville, Sean U. N. Wood, Chris Aimone et al. NeuroImage, 2022. Cites: paper

Enabling real-world EEG applications with deep learning
Hubert J. Banville. 2022. Cites: paper

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020
Shenda Hong, Wenrui Zhang, Chenxi Sun et al. Frontiers in Physiology, 2022. Cites: paper

Abdominal Electromyograms Mining: Breathing Patterns of Asleep Adults
Gennady P. Chuiko, Yevhen S. Darnapuk, Olga V. Dvornik et al. SSRN Electronic Journal, 2022. Cites: paper

Correlated diseases
Anirban Dutta Choudhury, Rohan Banerjee, Sanjay Kimbahune et al. Elsevier eBooks, 2022. Cites: paper

Recent Advancement in Sleep Technologies: A Literature Review on Clinical Standards, Sensors, Apps, and AI Methods
Gozde Cay, Vignesh Ravichandran, Shehjar Sadhu et al. IEEE Access, 2022. Cites: paper

Automatic detection of respiratory events during sleep from Polysomnography data using Layered Hidden Markov Model
Azadeh Sadoughi, Mohammad Bagher Shamsollahi, Emad Fatemizadeh. Physiological Measurement, 2021. Cites: paper

Development of a method for differential analysis of data on the arterial blood oxygenation in healthy adults
Gennady P. Chuiko, Yevhen S. Darnapuk. Eastern-European Journal of Enterprise Technologies, 2021. Cites: paper

Automatic Sleep Staging: Recent Development, Challenges, and Future Directions
Huy Phan, Kaare B. Mikkelsen. arXiv (Cornell University), 2021. Cites: paper

Design and Implementation of Automatic Sleep Staging based on ECG Signals
Weilun Yu, Dazhi Jiang, Wenxin Zeng et al. International Conference on Computational Intelligence and Security (CIS), 2021. Cites: paper

EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization
Igor Stančin, Nikolina Frid, Mario Cifrek et al. Sensors, 2021. Cites: paper

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals
Xiangyu Qian, Ye Qiu, Qingzu He et al. Brain Sciences, 2021. Cites: paper

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning
Ying‐Ren Chien, Cheng-Hsuan Wu, Hen‐Wai Tsao. Sensors, 2021. Cites: paper

Sleep Arousal Detection Using One Dimensional Local Binary Pattern-Based Convolutional Neural Network
Hasan Zan, Abdulnasır Yildiz. International Conference on Innovations in Intelligent Systems and Applications (INISTA), 2021. Cites: paper

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates
Luigi Fiorillo, Paolo Favaro, Francesca Dalia Faraci. arXiv (Cornell University), 2021. Cites: paper

“Devil’s stairs”, Poisson’s Statistics, and Patient Sorting via Variabilities for Oxygenation: All from Arterial Blood Gas Data
Gennady P. Chuiko, Yevhen S. Darnapuk, Olga V. Dvornik et al. bioRxiv (Cold Spring Harbor Laboratory), 2021. Cites: paper

Sleep Apnea Detection Using ResNet
Aditya Shanmugham, Bhamidipati V S Ananda Srivatsa, K Gopikrishnan et al. International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2021. Cites: paper

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics
Ivan Lazić, Riccardo Pernice, Tatjana Lončar-Turukalo et al. Entropy, 2021. Cites: paper

Boosting automated sleep staging performance in big datasets using population subgrouping
Samaneh Nasiri, Gari D. Clifford. SLEEP, 2021. Cites: paper

Artifact Detection and Correction in EEG data: A Review
Sari Saba-Sadiya, Tuka Alhanai, Mohammad M. Ghassemi. International IEEE/EMBS Conference on Neural Engineering, 2021. Cites: paper

U-Sleep: resilient high-frequency sleep staging
Mathias Perslev, Sune Darkner, Lykke Kempfner et al. npj Digital Medicine, 2021. Cites: paper

Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables
Qiao Li, Qichen Li, Ayse Selin Cakmak et al. Physiological Measurement, 2021. Cites: paper

Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia
Nooshin Bahador, Jarno Jokelainen, Seppo T. Mustola et al. Information Fusion, 2021. Cites: paper

Learning with self-supervision on EEG data
Alexandre Gramfort, Hubert J. Banville, Omar Chehab et al. International Winter Conference on Brain-Computer Interface (BCI), 2021. Cites: paper

Unsupervised EEG Artifact Detection and Correction
Sari Saba-Sadiya, Eric Chantland, Tuka Alhanai et al. Frontiers in Digital Health, 2021. Cites: paper

Automatic detection of non-apneic sleep arousal regions from polysomnographic recordings
Jamileh Karimi, Babak Mohammadzadeh Asl. Biomedical Signal Processing and Control, 2021. Cites: paper

DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal
Hongyang Li, Yuanfang Guan. Communications Biology, 2021. Cites: paper

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates
Luigi Fiorillo, Paolo Favaro, Francesca Dalia Faraci. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021. Cites: paper

Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM
Tanvir Mahmud, Ishtiaque Ahmed Khan, Talha Ibn Mahmud et al. IEEE Access, 2021. Cites: paper

Uncovering the structure of clinical EEG signals with self-supervised learning
Hubert J. Banville, Omar Chehab, Aapo Hyvärinen et al. Työväentutkimus Vuosikirja, 2021. Cites: paper

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
Huy Phan, Oliver Y. Chén, Minh C. Tran et al. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. Cites: paper

Prediction of Apnoea and Non-apnoea Arousals from the Polysomnogram using a Neural Network Classifier
Philip de Chazal, John Du, Nadi Sadr. Computing in cardiology, 2020. Cites: paper

Uncovering the structure of clinical EEG signals with self-supervised learning
Hubert J. Banville, Omar Chehab, Aapo Hyvärinen et al. Journal of Neural Engineering, 2020. Cites: paper

Obstructive Sleep Apnea Detection Using Sleep Architecture
Juan Liu, Qin Li, Yi Xin et al. IEEE International Conference on Mechatronics and Automation (ICMA), 2020. Cites: paper

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
Alexandra-Maria Tăuţan, Alessandro C. Rossi, Ruben de Francisco et al. Biomedizinische Technik/Biomedical Engineering, 2020. Cites: paper

Sleep Stage Classification From Polysomnography Signals by Using Long Short Term Memory
Hilal Sultan Duranoglu Tunc, İbrahim Yücel Özbek, Mehmet Ertuğrul. Signal Processing and Communications Applications Conference (SIU), 2020. Cites: paper

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