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

A Single-Channel Sleep Staging Method Based on Self-Supervised Learning
Wei Gao, Zhengqing Hu, Yanqing Liu et al. International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE), 2024. Cites: paper

Detecting Sleep Disorders in Polysomnography Data
Thiago Barral F. Reis, Michel Pompeu Tcheou, Felipe da Rocha Henriques. IEEE Latin America Symposium on Circuits and Systems (LASCAS), 2024. Cites: paper

Res-U-Net-Based Sleep Arousal Detection Using Limited Polysomnography Channels and Multi-Step Training Techniques
Mohammad Hasan Hosseini, Maryam Mohebbi. CSI International Symposium on Artificial Intelligence and Signal Processing, 2024. Cites: paper

Label-Efficient Sleep Staging Using Transformers Pretrained with Position Prediction
Sayeri Lala, Hanlin Goh, Christopher M. Sandino. IEEE International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC), 2024. Cites: paper

Sleep arousal detection for monitoring of sleep disorders using one-dimensional convolutional neural network-based U-Net and bio-signals
Priya Mishra, Aleena Swetapadma. Data Technologies and Applications, 2024. Cites: paper

Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice
Huijun Yue, Zhuqi Chen, Wenbin Guo et al. Sleep Medicine Reviews, 2024. Cites: paper

A Multi-scale Attention Network for Sleep Arousal Detection with Single-Channel ECG
Yidan Dai, Ye Lin, Wenjun Ma et al. Lecture notes in computer science, 2024. Cites: paper

Biomedizinische Signalverarbeitung für die automatisierte Erkennung von Schlafarousals, basierend auf Multi-Physiologischen Signalen mit Ensemble-Lernmethoden
Navabeh Sadat Jalili Shani, Mohammad Hassan Moradi. Springer eBooks, 2024. Cites: paper

Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study
Roksana Akter Raisa, Ayesha Siddika Rodela, Mohammad Abu Yousuf et al. IEEE Access, 2024. Cites: paper

DRLFormer: A Data Rebalancing Loss-Constrained Light Transformer for OSA Detection
Chengjian Li, Zhenghao Shi, Zhenzhen You et al. IEEE Transactions on Instrumentation and Measurement, 2024. Cites: paper

Macro-Sleep Staging With ECG-Derived Instantaneous Heart Rate and Respiration Signals and Multi-Input 1-D CNN–BiGRU
Roneel V. Sharan, Hiroki Takeuchi, Akifumi Kishi et al. IEEE Transactions on Instrumentation and Measurement, 2024. Cites: paper

MSleepNet: A Semi-Supervision-Based Multiview Hybrid Neural Network for Simultaneous Sleep Arousal and Sleep Stage Detection
Hongmei Liu, Haibo Zhang, Baozhu Li et al. IEEE Transactions on Instrumentation and Measurement, 2024. Cites: paper

Sleep Arousal Identification Based on Generative Adversarial Network and Multi-Head Attention Convolutional Network
Zhongxing Liu, Wenjuan Lu, Jinxi Wang et al. SSRN Electronic Journal, 2024. Cites: paper

SwinSleep: A Deep Learning Framework Advancing Overnight Sleep Staging Toward Clinical Practice
Di Zhang, Yichong She, Jinbo Sun et al. IEEE Transactions on Instrumentation and Measurement, 2024. Cites: paper

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review
Reza Soleimani, Jeffrey Barahona, Yuhan Chen et al. Physiologia, 2023. Cites: paper

Multimodal Polysomnography-Based Automatic Sleep Stage Classification via Multiview Fusion Network
Yiwei Lin, Mengying Wang, Fengdan Hu et al. IEEE Transactions on Instrumentation and Measurement, 2023. Cites: paper

Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review
Haifa Almutairi, Ghulam Mubashar Hassan, Amitava Datta. Applied Sciences, 2023. Cites: paper

Infimal convolution and AM-GM majorized total variation-based integrated approach for biosignal denoising
Vinit Kumar, Priya Ranjan Muduli. Signal Image and Video Processing, 2023. Cites: paper

Healthy Young POLes – HYPOL database with synchronised beat-to-beat heart rate and blood pressure signals
Przemysław Guzik, Tomasz Krauze, Andrzej Wykrętowicz et al. Journal of Medical Science, 2023. Cites: paper

Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models
L. Lechner, Asbjoern W. Helge, Esben Ahrens et al. Wearable and Implantable Body Sensor Networks, 2023. Cites: paper

MorpheusNet: Resource Efficient Sleep Stage Classifier for Embedded On-Line Systems
Ali Kavoosi, Morgan P. Mitchell, Raveen Kariyawasam et al. Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2023. Cites: paper

SelANet: decision-assisting selective sleep apnea detection based on confidence score
Beomjun Bark, Borum Nam, In Young Kim. BMC Medical Informatics and Decision Making, 2023. Cites: paper

Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series
Thea Brüsch, Mikkel Nørgaard Schmidt, Tommy Sonne Alstrøm. Machine learning for signal processing ..., 2023. Cites: paper

Security enhancement of the access control scheme in IoMT applications based on fuzzy logic processing and lightweight encryption
Ghada M. El‐Banby, Lamiaa A. Abou Elazm, Walid El‐Shafai et al. Complex & Intelligent Systems, 2023. Cites: paper

Single-Channel Ecg-Based Sleep Stage Classification With End-To-End Trainable Deep Neural Networks
Iksoo Choi, Wonyong Sung. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. Cites: paper

Derin Öğrenme ve Makine Öğrenmesi Yöntemleriyle Polisomnografi Sinyallerinden Uyku Evrelerinin Sınıflandırılması
Sinan Altun. Karadeniz Fen Bilimleri Dergisi, 2023. Cites: paper

Development of generalizable automatic sleep staging using heart rate and movement based on large databases
Joonnyong Lee, Hee Chan Kim, Yu Jin Lee et al. Biomedical Engineering Letters, 2023. Cites: paper

Sleep staging based on single-channel EEG and EOG with Tiny U-Net
Jingyi Lu, Chang Yan, Jianqing Li et al. Computers in Biology and Medicine, 2023. Cites: paper

Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG
Jash Shah, Amit Chougule, Vinay Chamola et al. Neurocomputing, 2023. Cites: paper

Deep convolutional architecture‐based hybrid learning for sleep arousal events detection through single‐lead EEG signals
Andia Foroughi, Fardad Farokhi, Fereidoun Nowshiravan Rahatabad et al. Brain and Behavior, 2023. Cites: paper

Generalisability of sleep stage classification based on interbeat intervals: validating three machine learning approaches on self-recorded test data
Stefan Kranzinger, Sebastian Baron, Christina Kranzinger et al. Behaviormetrika, 2023. Cites: paper

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review
Ziyu Liu, Azadeh Alavi, Minyi Li et al. Sensors, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: where are we now
Federico Del Pup, Manfredo Atzori. 2023. Cites: paper

U-Sleep’s resilience to AASM guidelines
Luigi Fiorillo, Giuliana Monachino, Julia van der Meer et al. npj Digital Medicine, 2023. Cites: paper

A Review on Detection and Correction of Artifacts from EEG Data
Sagar S. Motdhare, Garima Mathur. International Journal of Recent Technology and Engineering (IJRTE), 2023. Cites: paper

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis
Alexander Neergaard Zahid, Poul Jennum, Emmanuel Jean-Marie Mignot et al. IEEE Transactions on Biomedical Engineering, 2023. Cites: paper

Signal Processing and Machine Learning for Smart Sensing Applications
Ying‐Ren Chien, Mu Zhou, Ao Peng et al. Sensors, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: A Survey
Federico Del Pup, Manfredo Atzori. IEEE Access, 2023. Cites: paper

A Systematic Review of Electroencephalography Open Datasets and Their Usage With Deep Learning Models
Alberto Nogales, Álvaro José García-Tejedor. IEEE Access, 2023. Cites: paper

Biomedical Signal Processing for Automated Detection of Sleep Arousals Based on Multi-Physiological Signals with Ensemble Learning Methods
Navabeh Sadat Jalili Shani, Mohammad Hassan Moradi. Springer eBooks, 2023. Cites: paper

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles
Chih-Fan Kuo, Cheng-Yu Tsai, Wun-Hao Cheng et al. Digital Health, 2023. Cites: paper

Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram
"Franz Ehrlich, Johannes Bender, Hagen Malberg et al. Computing in cardiology, 2022. Cites: paper

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device
Yang Li, Jianqing Li, Chang Yan et al. Sensors, 2022. Cites: paper

An Automatic Sleep Arousal Detection Method by Enhancing U-Net with Spatial-channel Attention
Yen-Ning Su, Chih‐En Kuo. IEEE International Conference on Big Data (BigData), 2022. Cites: paper

Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG
Zhengling He, Minfang Tang, Peng Wang et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Time-Frequency Ridge Analysis of Sleep Stage Transitions
Christopher McCausland, Pardis Biglarbeigi, Raymond Bond et al. IEEE Signal Processing in Medicine and Biology Symposium, 2022. Cites: paper

SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography
Kevin Kotzen, Peter H Charlton, Sharon Salabi et al. IEEE Journal of Biomedical and Health Informatics, 2022. Cites: paper

U-Sleep: resilient to AASM guidelines
Luigi Fiorillo, Giuliana Monachino, Julia van der Meer et al. Research Square, 2022. Cites: paper

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