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

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

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Shenda Hong, Yuxi Zhou, Junyuan Shang et al. arXiv (Cornell University), 2019. 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

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

Reproducibility in Machine Learning for Health
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek et al. arXiv (Cornell University), 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

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

DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning
Hongyang Li, Yuanfang Guan. bioRxiv (Cold Spring Harbor Laboratory), 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

Using Auxiliary Loss to Improve Sleep Arousal Detection With Neural Network
Bálint Varga, Márton Görög, Péter Hajas. Computing in cardiology, 2018. Cites: paper

Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram
Qiao Li, Qichen Li, Chengyu Liu et al. Physiological Measurement, 2018. Cites: paper

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