Publications from You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018

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The papers listed below were presented at Computers in Cardiology 2018. Please cite this publication when referencing any of these papers. These papers have been made available under the terms of the Creative Commons Attribution License 3.0 (CCAL). We wish to thank all of the authors for their contributions.

This paper is an introduction to the challenge topic, with a summary of the challenge results and a discussion of their implications:

Mohammad M Ghassemi, Benjamin E Moody, Li-wei H Lehman, Christopher Song, Qiao Li, Haoqi Sun, Roger G Mark, M Brandon Westover, Gari D Clifford. You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018.

The remaining papers were presented by participants in the Challenge, who describe their approaches to the challenge problem.

Publications listed alphabetically by author

  1. Tanuka Bhattacharjee, Deepan Das, Shahnawaz Alam, Achuth Rao M V, Prasanta Kumar Ghosh, Ayush Ranjan Lohani, Rohan Banerjee, Anirban Dutta Choudhury, Arpan Pal. SleepTight: Identifying Sleep Arousals Using Inter and Intra-Relation of Multimodal Signals
  2. Jia Dongya, Shengfeng Yu, Cong Yan, Wei Zhao, Jing Hu, Hongmei Wang, Tianyuan You. Deep Learning with Convolutional Neural Networks for Sleep Arousal Detection
  3. Runnan He, Kuanquan Wang, Yang Liu, Na Zhao, Yongfeng Yuan, Qince Li, Henggui Zhang. Identification of Arousals With Deep Neural Networks Using Different Physiological Signals
  4. Matthew Howe-Patterson, Bahareh Pourbabaee, Frederic Benard. Automated Detection of Sleep Arousals From Polysomnography Data Using a Dense Convolutional Neural Network
  5. Ivan Lazić, Nikša Jakovljević, Danica Despotović, Tatjana Lončar-Turukalo. Automatic Detection of Respiratory Effort Related Arousals From Polysomnographic Recordings
  6. Haoqi Li, Qineng Cao, Yizhou Zhong, Yun Pan. Sleep Arousal Detection Using End-to-End Deep Learning Method Based on Multi-Physiological Signals
  7. Daniel Miller, Andrew Ward, Nicholas Bambos. Automatic Sleep Arousal Identification From Physiological Waveforms Using Deep Learning
  8. Naimahmed Nesaragi, Shubha Majumder, Ashish Sharma, Kouhyar Tavakolian, Shivnarayan Patidar. Application of Recurrent Neural Network for the Prediction of Target Non-Apneic Arousal Regions in Physiological Signals
  9. Saman Parvaneh, Jonathan Rubin, Ali Samadani, Gajendra Katuwal. Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals
  10. Andrea Patane, Shadi Ghiasi, Enzo Pasquale Scilingo, Marta Kwiatkowska. Automated Recognition of Sleep Arousal Using Multimodal and Personalized Deep Ensembles of Neural Networks
  11. Filip Plesinger, Petr Nejedly, Ivo Viscor, Petr Andrla, Josef Halamek, Pavel Jurak. Automated Sleep Arousal Detection Based on EEG Envelograms
  12. Shahab Rezaei, Sadaf Moharreri, Nader Jafarnia Dabanloo, Saman Parvaneh. Age and Changes in Extracted Features of Lagged Poincare Plot
  13. Nadi Sadr and Philip de Chazal. Automatic Scoring of Non-Apnoea Arousals Using the Polysomnogram
  14. Sven Schellenberger, Kilin Shi, Melanie Mai, Jan Philipp Wiedemann, Tobias Steigleder, Björn Eskofier, Robert Weigel, Alexander Kölpin. Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks
  15. Yinghua Shen. Effectiveness of a Convolutional Neural Network in Sleep Arousal Classification Using Multiple Physiological Signals.
  16. Niranjan Sridhar and Ali Shoeb. Evaluating Convolutional and Recurrent Neural Network Architectures for Respiratory-Effort Related Arousal Detection during Sleep.
  17. Sandya Subramanian, Shubham Chamadia, Sourish Chakravarty. Arousal Detection in Obstructive Sleep Apnea using Physiology-Driven Features.
  18. János Szalma, András Bánhalmi, Vilmos Bilicki. Detection of Respiratory Effort-Related Arousals Using a Hidden Markov Model and Random Decision Forest.
  19. Heiðar Már Þráinsson, Hanna Ragnarsdóttir, Guðni Fannar Kristjansson, Bragi Marinósson, Eysteinn Finnsson, Eysteinn Gunnlaugsson, Sigurður Ægir Jónsson, Jón Skírnir Ágústsson, Halla Helgadóttir. Automatic Detection of Target Regions of Respiratory Effort-Related Arousals Using Recurrent Neural Networks.
  20. Edwar Macias Toro, Antoni Morell, Javier Serrano, Jose Lopez Vicario. Knowledge extraction based on wavelets and DNN for classification of physiological signals: Arousals case.
  21. Bálint Varga, Márton Görög, Péter Hajas. Using Auxiliary Loss to Improve Sleep Arousal Detection With Neural Network.
  22. Philip Warrick and Masun Nabhan Homsi. Sleep Arousal Detection From Polysomnography Using the Scattering Transform and Recurrent Neural Networks.
  23. Morteza Zabihi, Ali Bahrami Rad, Simo Särkkä, Serkan Kiranyaz, Aggelos K. Katsaggelos, Moncef Gabbouj. Automatic Sleep Arousal Detection Using Multimodal Biosignal Analysis.

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Updated Saturday, 23 February 2019 at 02:17 BRT

PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.