Publications from You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018
The following paper describes the PhysioNet/Computing in Cardiology Challenge. Please cite this publication when referencing the Challenge.
You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018.
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. Computers in Cardiology 2018.
The following papers were presented at the Computing in Cardiology Conference.
- 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
- Jia Dongya, Shengfeng Yu, Cong Yan, Wei Zhao, Jing Hu, Hongmei Wang, Tianyuan You. Deep Learning with Convolutional Neural Networks for Sleep Arousal Detection
- 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
- Matthew Howe-Patterson, Bahareh Pourbabaee, Frederic Benard. Automated Detection of Sleep Arousals From Polysomnography Data Using a Dense Convolutional Neural Network
- Ivan Lazić, Nikša Jakovljević, Danica Despotović, Tatjana Lončar-Turukalo. Automatic Detection of Respiratory Effort Related Arousals From Polysomnographic Recordings
- Haoqi Li, Qineng Cao, Yizhou Zhong, Yun Pan. Sleep Arousal Detection Using End-to-End Deep Learning Method Based on Multi-Physiological Signals
- Daniel Miller, Andrew Ward, Nicholas Bambos. Automatic Sleep Arousal Identification From Physiological Waveforms Using Deep Learning
- 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
- Saman Parvaneh, Jonathan Rubin, Ali Samadani, Gajendra Katuwal. Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals
- Andrea Patane, Shadi Ghiasi, Enzo Pasquale Scilingo, Marta Kwiatkowska. Automated Recognition of Sleep Arousal Using Multimodal and Personalized Deep Ensembles of Neural Networks
- Filip Plesinger, Petr Nejedly, Ivo Viscor, Petr Andrla, Josef Halamek, Pavel Jurak. Automated Sleep Arousal Detection Based on EEG Envelograms
- Shahab Rezaei, Sadaf Moharreri, Nader Jafarnia Dabanloo, Saman Parvaneh. Age and Changes in Extracted Features of Lagged Poincare Plot
- Nadi Sadr and Philip de Chazal. Automatic Scoring of Non-Apnoea Arousals Using the Polysomnogram
- 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
- Yinghua Shen. Effectiveness of a Convolutional Neural Network in Sleep Arousal Classification Using Multiple Physiological Signals.
- Niranjan Sridhar and Ali Shoeb. Evaluating Convolutional and Recurrent Neural Network Architectures for Respiratory-Effort Related Arousal Detection during Sleep.
- Sandya Subramanian, Shubham Chamadia, Sourish Chakravarty. Arousal Detection in Obstructive Sleep Apnea using Physiology-Driven Features.
- János Szalma, András Bánhalmi, Vilmos Bilicki. Detection of Respiratory Effort-Related Arousals Using a Hidden Markov Model and Random Decision Forest.
- 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.
- Edwar Macias Toro, Antoni Morell, Javier Serrano, Jose Lopez Vicario. Knowledge extraction based on wavelets and DNN for classification of physiological signals: Arousals case.
- Bálint Varga, Márton Görög, Péter Hajas. Using Auxiliary Loss to Improve Sleep Arousal Detection With Neural Network.
- Philip Warrick and Masun Nabhan Homsi. Sleep Arousal Detection From Polysomnography Using the Scattering Transform and Recurrent Neural Networks.
- Morteza Zabihi, Ali Bahrami Rad, Simo Särkkä, Serkan Kiranyaz, Aggelos K. Katsaggelos, Moncef Gabbouj. Automatic Sleep Arousal Detection Using Multimodal Biosignal Analysis.