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

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

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

Deep learning for automated sleep staging using instantaneous heart rate
Niranjan Sridhar, Ali H. Shoeb, Philip Stephens et al. npj Digital Medicine, 2020. Cites: paper

An Automatic Sleep Staging Model Combining Feature Learning and Sequence Learning
Yinghao Li, Zhenghui Gu, Zichao Lin et al. International Conference on Advanced Computational Intelligence (ICACI), 2020. Cites: paper

Automated Annotation of Polysomnogram Epochs for Apnoea and Non-apnoea Arousals
Philip de Chazal, Nadi Sadr. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020. Cites: paper

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings
Sajila Wickramaratne, Md Shaad Mahmud. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020. Cites: paper

Automatic Sleep Stage Detection: A Study on the Influence of Various PSG Input Signals
Alexandra-Maria Tăuţan, Alessandro C. Rossi, Ruben de Francisco et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020. Cites: paper

Information-Theoretic Analysis of Cardiorespiratory Interactions During Apneic Events in Sleep
Ivan Lazić, Tatjana Lončar-Turukalo, Luca Faes. Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), 2020. Cites: paper

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Shenda Hong, Yuxi Zhou, Junyuan Shang et al. Computers in Biology and Medicine, 2020. Cites: paper

Reinventing polysomnography in the age of precision medicine
Diane C Lim, Diego R. Mazzotti, Kate Sutherland et al. Sleep Medicine Reviews, 2020. Cites: paper

Classification of Sleep Arousal using Compact CNN
Ahmed M. Eldaraa, Hamza Baali, Abdesselam Bouzerdoum et al. IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020. Cites: paper

Automatic Sleep Arousals Detection From Polysomnography Using Multi-Convolution Neural Network and Random Forest
Yitian Liu, Hongxing Liu, Bufang Yang. IEEE Access, 2020. Cites: paper

Classification of Sleep Apnea with Artificial Intelligence
少杰张. Biophysics, 2020. Cites: paper

Multimodal Sleep Signals-Based Automated Sleep Arousal Detection
Guangxin Zhou, Runzhi Li, Shuo Zhang et al. IEEE Access, 2020. Cites: paper

U-Sleep: A Deep Neural Network for Automated Detection of Sleep Arousals Using Multiple PSGs
Shenglan Yang, Bijue Jia, Yao Chen et al. Lecture notes in computer science, 2020. Cites: paper

Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram
Philip de Chazal, Nadi Sadr. Physiological Measurement, 2019. Cites: paper

Supervised and unsupervised machine learning for automated scoring of sleep–wake and cataplexy in a mouse model of narcolepsy
Ioannis Exarchos, Anna Rogers, Lauren M. Aiani et al. SLEEP, 2019. Cites: paper

Automatic Sleep Stage Detection using a Single Channel Frontal EEG
Alexandra-Maria Tăuţan, Alessandro C. Rossi, Ruben de Francisco et al. E-Health and Bioengineering Conference (EHB), 2019. Cites: paper

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner et al. Research at the University of Copenhagen (University of Copenhagen), 2019. Cites: paper

Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao et al. arXiv (Cornell University), 2019. Cites: paper

SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series
Bahareh Pourbabaee, Matthew Howe-Patterson, Matthew Patterson et al. Physiological Measurement, 2019. Cites: paper

Trend Statistics Network and Channel invariant EEG Network for sleep arousal study
M V Achuth Rao, Prasanta Kumar Ghosh, Tanuka Bhattacharjee et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. Cites: paper

Hybrid scattering-LSTM networks for automated detection of sleep arousals
Philip A. Warrick, Vincent Lostanlen, Masun Nabhan Homsi. Physiological Measurement, 2019. Cites: paper

Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy, Hubert J. Banville, Isabela Albuquerque et al. Journal of Neural Engineering, 2019. Cites: paper

Advanced polysomnographic analysis for OSA: A pathway to personalized management?
Philip de Chazal, Kate Sutherland, Peter A. Cistulli. Respirology, 2019. Cites: paper

Bidirectional LSTM with MFCC Feature Extraction for Sleep Arousal Detection in Multi-channel Signal Data
Hyunseob Kim, Tae Joon Jun, Giang Nguyen et al. Lecture notes in computer science, 2019. Cites: paper

Application of Recurrent Neural Network for the Prediction of Target Non-Apneic Arousal Regions in Physiological Signals
Naimahmed Nesaragi, Shubha Majumder, Ashish Sharma et al. Computing in cardiology, 2018. Cites: paper

Arousal Detection in Obstructive Sleep Apnea using Physiology-Driven Features
Sandya Subramanian, Shubham Chamadia, Sourish Chakravarty. Computing in cardiology, 2018. Cites: paper

Automated Detection of Sleep Arousals From Polysomnography Data Using a Dense Convolutional Neural Network
Matthew Howe-Patterson, Bahareh Pourbabaee, Frederic Benard. Computing in cardiology, 2018. Cites: paper

Automated Recognition of Sleep Arousal Using Multimodal and Personalized Deep Ensembles of Neural Networks
Andrea Patanè, Shadi Ghiasi, Enzo Pasquale Scilingo et al. Computing in cardiology, 2018. Cites: paper

Automated Sleep Arousal Detection Based on EEG Envelograms
Filip Plešinger, Petr Nejedlý, Ivo Viščor et al. Computing in cardiology, 2018. Cites: paper

Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals
Saman Parvaneh, Jonathan Rubin, Ali Samadani et al. Computing in cardiology, 2018. Cites: paper

Automatic Detection of Respiratory Effort Related Arousals From Polysomnographic Recordings
Ivan Lazić, Niksa M. Jakovljevic, Danica Despotović et al. Computing in cardiology, 2018. Cites: paper

Automatic Detection of Target Regions of Respiratory Effort-Related Arousals Using Recurrent Neural Networks
H.M. þráinsson, Hanna Ragnarsdóttir, Guðni Fannar Kristjánsson et al. Computing in cardiology, 2018. Cites: paper

Automatic Scoring of Non-Apnoea Arousals Using the Polysomnogram
Nadi Sadr, Philip de Chazal. Computing in cardiology, 2018. Cites: paper

Automatic Sleep Arousal Detection Using Multimodal Biosignal Analysis
Morteza Zabihi, Ali Bahrami Rad, Simo Särkkä et al. Computing in cardiology, 2018. Cites: paper

Evaluating Convolutional and Recurrent Neural Network Architectures for Respiratory-Effort Related Arousal Detection during Sleep
Niranjan Sridhar, Ali H. Shoeb. Computing in cardiology, 2018. Cites: paper

Knowledge extraction based on wavelets and DNN for classification of physiological signals: Arousals case
Edwar Macias, Antoni Morell, Javier Serrano et al. Computing in cardiology, 2018. Cites: paper

SleepTight: Identifying Sleep Arousals Using Inter and Intra-Relation of Multimodal Signals
Tanuka Bhattacharjee, Deepan Das, Shahnawaz Alam et al. Computing in cardiology, 2018. Cites: paper

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