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

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique
Christopher McCausland, Pardis Biglarbeigi, Raymond Bond et al. Expert Systems with Applications, 2024. Cites: paper

A Multi-modal Framework with Contrastive Learning and Sequential Encoding for Enhanced Sleep Stage Detection
Zehui Wang, Zhihan Zhang, Hongtao Wang. Lecture notes in computer science, 2024. Cites: paper

Sleep Apnea Identification Based on EEG Signals Using Hybrid Spatio-Temporal Deep Learning
Romy Syaputra Wijaya, Esmeralda C. Djamal, Fatan Kasyidi. International Conference on Computer, Control, Informatics and its Applications, 2024. Cites: paper

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability
Nantawachara Jirakittayakorn, Yodchanan Wongsawat, Somsak Mitrirattanakul. Computers in Biology and Medicine, 2024. Cites: paper

State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset
Franz Ehrlich, Tony Sehr, Moritz Brandt et al. Scientific Reports, 2024. Cites: paper

Sleep Stage Classification With Multi-Modal Fusion and Denoising Diffusion Model
Xu Xu, Fengyu Cong, Yongyong Chen et al. IEEE Journal of Biomedical and Health Informatics, 2024. Cites: paper

Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram
Sowmya Muchukunte Ramaswamy, Merel H. Kuizenga, Maud A. S. Weerink et al. PLoS ONE, 2024. Cites: paper

A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification
Sergio Kazatzidis, Siamak Mehrkanoon. International Joint Conference on Neural Networks (IJCNN), 2024. Cites: paper

Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations
Fernanda B. Silva, Luisa Fernanda Suárez Uribe, Felipe X. Cepeda et al. Sleep Medicine, 2024. Cites: paper

Securing Internet-of-Medical-Things networks using cancellable ECG recognition
Samia A. El-Moneim Kabel, Ghada M. El‐Banby, Lamiaa A. Abou Elazm et al. Scientific Reports, 2024. Cites: paper

Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review
Tanvir Islam, Peter Yigitcan Washington. Biosensors, 2024. Cites: paper

Generalizable Sleep Staging via Multi-Level Domain Alignment
Jiquan Wang, Sha Zhao, Haiteng Jiang et al. Proceedings of the AAAI Conference on Artificial Intelligence, 2024. Cites: paper

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 American 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

An Innovative Approach for the Prediction of Future Arrhythmia through T-wave Alternans on Surface Electrocardiogram (ECG)
Ali Farhan, Ijaz Rasul, Sahar Fazal, Azmat Hayat et al. Pakistan Armed Forces Medical Journal, 2023. 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

Advances in Self-Supervised Learning : applications to neuroscience and sample-efficiency
l'Émir Omar Chéhab. theses.fr (ABES), 2023. Cites: paper

SleePyCo: Automatic sleep scoring with feature pyramid and contrastive learning
Seongju Lee, Yeonguk Yu, Seunghyeok Back et al. Expert Systems with Applications, 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

Abdominal electromyograms mining: breathing patterns of asleep adults
Gennady P. Chuiko, Yevhen S. Darnapuk, Olga V. Dvornik et al. RADIOELECTRONIC AND COMPUTER SYSTEMS, 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

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