Article
Engineering, Biomedical
Antoine Guillot, Valentin Thorey
Summary: Diagnosis of sleep disorders relies on analysis of PSG records, with RobustSleepNet, a deep learning model for automatic sleep stage classification, proving to be effective in handling different PSG montages and demographic changes. Trained on a diverse dataset, RobustSleepNet shows promising results in performance on unseen data, offering the potential for high-quality automatic sleep staging in various clinical setups.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Review
Biophysics
Huy Phan, Kaare Mikkelsen
Summary: Modern deep learning has the potential to transform clinical studies of human sleep and reduce the workload of clinicians. Automatic sleep staging systems have achieved similar performance to human experts, but face challenges in clinical adoption.
PHYSIOLOGICAL MEASUREMENT
(2022)
Article
Clinical Neurology
Hans van Gorp, Iris A. M. Huijben, Pedro Fonseca, Ruud J. G. van Sloun, Sebastiaan Overeem, Merel M. van Gilst
Summary: This study provides a theoretical framework to discuss and analyze the uncertainty in sleep staging. By introducing two variants of uncertainty and understanding their types and sources in sleep staging, recommendations are made to improve sleep staging in the future.
Article
Engineering, Biomedical
Huafeng Wang, Chonggang Lu, Qi Zhang, Zhimin Hu, Xiaodong Yuan, Pingshu Zhang, Wanquan Liu
Summary: Sleep is crucial for mental and physical health, and sleep disorders can greatly impact people's lives. Sleep staging plays a vital role in diagnosing sleep disorders. Automatic sleep staging using single-channel EEG has become a popular research topic. This paper proposes a multiscale dual attention network based on raw EEG to extract features and achieves state-of-the-art results.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Akseli Leino, Henri Korkalainen, Laura Kalevo, Sami Nikkonen, Samu Kainulainen, Alexander Ryan, Brett Duce, Kirsi Sipila, Jari Ahlberg, Johanna Sahlman, Tomi Miettinen, Susanna Westeren-Punnonen, Esa Mervaala, Juha Toyras, Sami Myllymaa, Timo Leppanen, Katja Myllymaa
Summary: This study utilized deep learning-based automated sleep staging for EEG signals acquired with the AES. The neural network model demonstrated high accuracy in determining sleep stages, comparable to standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of in-home PSG studies.
Article
Chemistry, Analytical
Amelia A. Casciola, Sebastiano K. Carlucci, Brianne A. Kent, Amanda M. Punch, Michael A. Muszynski, Daniel Zhou, Alireza Kazemi, Maryam S. Mirian, Jason Valerio, Martin J. McKeown, Haakon B. Nygaard
Summary: Sleep disturbances are common in neurodegenerative disorders, and a deep learning model for automated sleep staging using portable EEG devices can help overcome the limitations of overnight polysomnography.
Article
Computer Science, Artificial Intelligence
Xingfeng Lv, Jun Ma, Jinbao Li, Qianqian Ren
Summary: Sleep stage classification is important for evaluating sleep quality. However, many methods ignore the impact of obstructive sleep apnea (OSA) on sleep staging. In this study, a structured sleep staging network (SSleepNet) based on OSA was proposed to improve sleep staging performance by learning comprehensive features and transfer relationships.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Jaeun Phyo, Wonjun Ko, Eunjin Jeon, Heung-Il Suk
Summary: This study proposes a novel deep neural-network structure called TransSleep, which captures distinctive local temporal patterns and classifies confusing stages in transitioning epochs. The results show that TransSleep achieves promising performance in automatic sleep staging and demonstrates state-of-the-art performance on two publicly available datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Liqiang Zhu, Changming Wang, Zhihui He, Yuan Zhang
Summary: With the advancement of edge artificial intelligence, a lightweight automatic sleep staging method for children has been developed, utilizing 1D convolutional neural networks and LSTM technology. The CSleepNet model shows high accuracy in experiments and has the potential to be widely used in the classification of other physiological signals.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Kevin Kotzen, Peter H. Charlton, Sharon Salabi, Lea Amar, Amir Landesberg, Joachim A. Behar
Summary: Staging is crucial in diagnosing sleep disorders and managing sleep health. This study proposes an automated approach using raw photoplethysmography (PPG) time series and deep learning for four-class sleep staging. The developed SleepPPG-Net model achieves state-of-the-art performance and opens possibilities for the development of wearable devices for clinical applications.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Neurosciences
Lijuan Duan, Mengying Li, Changming Wang, Yuanhua Qiao, Zeyu Wang, Sha Sha, Mingai Li
Summary: Sleep staging is crucial for diagnosing and treating sleep disorders, but manually doing so is laborious and time-consuming. Researchers have developed an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and EOG signals, achieving state-of-the-art performance and aligning with expert diagnoses.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Multidisciplinary Sciences
Rockelle S. Guthrie, Davide Ciliberti, Emily A. Mankin, Gina R. Poe
Summary: A study found that the neocortex and hippocampus in the human brain may exhibit asynchronous state transitions during sleep, which questions the assessment of the functions and drivers of sleep states throughout the brain from a whole-brain perspective.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Computer Science, Interdisciplinary Applications
Yousef Rezaei Tabar, Kaare B. Mikkelsen, Mike Lind Rank, Martin Christian Hemmsen, Preben Kidmose
Summary: This study aimed to represent sleep EEG patterns using a minimum number of features without significant loss in performance. Through feature selection algorithms, it was found that 5 to 11 features could represent the whole feature set without performance loss. Features were divided into groups, with relative power features identified as the most informative.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Biochemical Research Methods
Yamei Li, Caijing Peng, Yinkai Zhang, Yuan Zhang, Benny Lo
Summary: This paper proposes a Bi-Stream Adversarial Learning network (BiSALnet) for pediatric sleep staging, which generates pseudo-labels with higher confidence through adversarial learning and semi-supervised methods. The model combines symmetric positive definite manifold structure and attention feature fusion module to improve the performance on local pediatric dataset and a well-known public dataset.
Article
Computer Science, Information Systems
Chuanhao Zhang, Wenwen Yu, Yamei Li, Hongqiang Sun, Yuan Zhang, Maarten De Vos
Summary: In this study, a co-attention meta sleep staging network (CMS2-net) is proposed to address two key challenges in automatic sleep disorder detection, achieving state-of-the-art semi-supervised learning results on both public and local datasets.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Engineering, Biomedical
Tsvetomira Tsoneva, Gary Garcia-Molina, Peter Desain
JOURNAL OF NEURAL ENGINEERING
(2015)
Article
Engineering, Biomedical
Gary Garcia-Molina, Tsvetomira Tsoneva, Jeff Jasko, Brenda Steele, Antonio Aquino, Keith Baher, Sander Pastoor, Stefan Pfundtner, Lynn Ostrowski, Barbara Miller, Noah Papas, Brady Riedner, Giulio Tononi, David P. White
JOURNAL OF NEURAL ENGINEERING
(2018)
Article
Multidisciplinary Sciences
Tsvetomira Tsoneva, Gary Garcia-Molina, Peter Desain
Summary: Through studying the dynamic properties of EEG in 32 subjects, we found that SSVEPs in the gamma frequency band originate in the primary visual cortex, Brodmann areas 17, 18, and 19, with minor contributions from central and frontal sites. Additionally, our research showed a progressive phase shift of SSVEPs over the cortex, with higher input frequencies leading to faster propagation speeds.
SCIENTIFIC REPORTS
(2021)
Article
Clinical Neurology
Charmaine Diep, Gary Garcia-Molina, Jeff Jasko, Jessica Manousakis, Lynn Ostrowski, David White, Clare Anderson
Summary: The study shows that consecutive nights of acoustic stimulation can enhance slow wave activity, improve alertness and attention, indicating potential benefits for individuals with chronic sleep restriction.
Article
Clinical Neurology
Charmaine Diep, Suzanne Ftouni, Sean P. A. Drummond, Gary Garcia-Molina, Clare Anderson
Summary: Acoustic stimulation enhances slow wave sleep and improves cardiac outcomes. This study investigated the effect of an acoustic, slow wave enhancing device on heart rate variability in healthy middle-aged males. The results showed that acoustic slow wave sleep enhancement can improve heart rate variability.
JOURNAL OF SLEEP RESEARCH
(2022)
Article
Chemistry, Analytical
Farzad Siyahjani, Gary Garcia Molina, Shawn Barr, Faisal Mushtaq
Summary: The performance of the Sleep Number smart bed in estimating heart rate, breathing rate, and sleep vs. wake states was evaluated in comparison to polysomnography. The study found a high correlation between the smart bed and polysomnography in these metrics, as well as good agreement in sleep vs. wake classification. Therefore, the Sleep Number smart bed can reliably measure human sleep characteristics.
Proceedings Paper
Engineering, Biomedical
Gary Garcia-Molina
Summary: This study examines the relationship between EEG, heart rate, and heart rate variability during sleep, proposing a model to characterize their relationship in the polar-coordinate domain. Group and individual-level analyses demonstrate high correlations between the polar-coordinate transformations of SWA and HR, offering a convenient way to monitor sleep slow-wave activity.
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
(2021)
Proceedings Paper
Engineering, Biomedical
Gary Garcia-Molina, Boomika Kalyan, Antonio Aquino
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
(2020)
Proceedings Paper
Engineering, Biomedical
Gary Garcia-Molina, Tsvetomira Tsoneva, Adam Neff, Jesse Salazar, Erik Bresch, Ulf Grossekathofer, Sander Pastoor, Antonio Aquino
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Gary Garcia-Molina, Keith Baehr, Brenda Steele, Tsvetomira Tsoneva, Stefan Pfundtner, Brady Riedner, David P. White, Giulio Tononi
2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
(2017)
Proceedings Paper
Engineering, Biomedical
Gary Garcia-Molina, Sreeram Vissapragada, Anandi Mahadevan, Robert Goodpaster, Brady Riedner, Michele Bellesi, Giulio Tononi
2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2016)
Proceedings Paper
Engineering, Biomedical
Gary Garcia-Molina, Michele Bellesi, Brady Riedner, Sander Pastoor, Stefan Pfundtner, Giulio Tononi
2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2015)
Proceedings Paper
Engineering, Biomedical
Gary Garcia-Molina, Piotr Bialas
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2013)
Proceedings Paper
Engineering, Biomedical
Tsvetomira Tsoneva, Gary Garcia-Molina, Jaap van de Sant, Jason Farquhar
2013 6TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)
(2013)