Revolutionary AI Model Analyzes Full Night of Sleep with High Accuracy in Largest Study to Date

New AI model analyzes full night of sleep with high accuracy in largest study of its kind

Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night's sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. Details on their findings were reported in the March 13online issue of the journal Sleep. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks. Current sleep analysis often relies on human experts manually scoring short segments of sleep data or using AI models that are not capable of analyzing a patient's entire night of sleep. This new approach, developed using thousands of sleep recordings, takes a more comprehensive view. By training on full-length sleep data, the model can recognize sleep patterns throughout the night and across different populations and settings, offering a standardized and scalable method for sleep research and clinical use, say the investigators. "This is a step forward in AI-assisted sleep analysis and interpretation," says first author Benjamin Fox, a PhD candidate at the Icahn School of Medicine at Mount Sinai in the Artificial Intelligence and Emerging Technologies Training Area. "By leveraging AI in this way, we can learn relevant clinical features directly from sleep study signal data and use them for sleep scoring and, in the future, other clinical applications such as detecting sleep apnea or assessing health risks linked to sleep quality." The model was built using a large dataset of sleep studies (polysomnograms) that measure key physiological signals, including brain activity, muscle tone, heart rate, and breathing patterns. Unlike traditional AI models, which analyze only short, 30-second segments, this new model considers the entire night of sleep, capturing more detailed and nuanced patterns. Further, the model is trained via a method known as self-supervision, which helps learn relevant clinical features from physiological signals without using human labeled outcomes. "Our findings suggest that AI could transform how we study and understand sleep," says co-senior corresponding author Ankit Parekh, PhD, Assistant Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai, and Director of the Sleep and Circadian Analysis Group at Mount Sinai. "Our next goal is to refine the technology for clinical applications, such as identifying sleep-related health risks more efficiently." The researchers emphasize that this AI tool, while promising, would not replace clinical expertise. Instead, it would serve as a powerful aid for sleep specialists, helping to speed up and standardize sleep analysis. Next, the team's research aims to expand its capabilities beyond sleep-stage classification to detecting sleep disorders and predicting health outcomes. "This AI-driven approach has the potential to revolutionize sleep research," says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, Director of the Hasso Plattner Institute for Digital Health, and the Irene and Dr. Arthur M. Fishberg Professor of Medicine. Dr. Nadkarni is also the inaugural Chief of the Division of Data-Driven and Digital Medicine and Co-Director of the Mount Sinai Clinical Intelligence Center. "By analyzing entire nights of sleep with greater consistency, we can uncover deeper insights into sleep health and its connection to overall well-being."

New AI tool revolutionizes sleep analysis with comprehensive sleep data

Mount Sinai Health SystemMar 17 2025 Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night's sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. Details on their findings were reported in the March 13 online issue of the journal Sleep [https://doi.org/10.1093/sleep/zsaf061]. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks. Current sleep analysis often relies on human experts manually scoring short segments of sleep data or using AI models that are not capable of analyzing a patient's entire night of sleep. This new approach, developed using thousands of sleep recordings, takes a more comprehensive view. By training on full-length sleep data, the model can recognize sleep patterns throughout the night and across different populations and settings, offering a standardized and scalable method for sleep research and clinical use, say the investigators. This is a step forward in AI-assisted sleep analysis and interpretation. By leveraging AI in this way, we can learn relevant clinical features directly from sleep study signal data and use them for sleep scoring and, in the future, other clinical applications such as detecting sleep apnea or assessing health risks linked to sleep quality." Benjamin Fox, first author, a PhD candidate at the Icahn School of Medicine at Mount Sinai in the Artificial Intelligence and Emerging Technologies Training Area The model was built using a large dataset of sleep studies (polysomnograms) that measure key physiological signals, including brain activity, muscle tone, heart rate, and breathing patterns. Unlike traditional AI models, which analyze only short, 30-second segments, this new model considers the entire night of sleep, capturing more detailed and nuanced patterns. Further, the model is trained via a method known as self-supervision, which helps learn relevant clinical features from physiological signals without using human labeled outcomes. "Our findings suggest that AI could transform how we study and understand sleep," says co-senior corresponding author Ankit Parekh, PhD, Assistant Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai, and Director of the Sleep and Circadian Analysis Group at Mount Sinai. "Our next goal is to refine the technology for clinical applications, such as identifying sleep-related health risks more efficiently." The researchers emphasize that this AI tool, while promising, would not replace clinical expertise. Instead, it would serve as a powerful aid for sleep specialists, helping to speed up and standardize sleep analysis. Next, the team's research aims to expand its capabilities beyond sleep-stage classification to detecting sleep disorders and predicting health outcomes. "This AI-driven approach has the potential to revolutionize sleep research," says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, Director of the Hasso Plattner Institute for Digital Health, and the Irene and Dr. Arthur M. Fishberg Professor of Medicine. Dr. Nadkarni is also the inaugural Chief of the Division of Data-Driven and Digital Medicine and Co-Director of the Mount Sinai Clinical Intelligence Center. "By analyzing entire nights of sleep with greater consistency, we can uncover deeper insights into sleep health and its connection to overall well-being." The paper is titled "A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages." The study's authors, as listed in the journal, are Benjamin Fox, Joy Jiang, Sajila Wickramaratne, Patricia Kovatch, Mayte Suarez-Farinas, Neomi A. Shah, Ankit Parekh, and Girish N. Nadkarni. Mount Sinai Health System Journal reference: Fox, B., et al. (2025). A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages. Sleep. doi.org/10.1093/sleep/zsaf061.

New AI model analyzes full night of sleep with high accuracy in largest study of its kind

Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night's sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. Details of their findings were reported in the March 13 online issue of the journal SLEEP. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks. Current sleep analysis often relies on human experts manually scoring short segments of sleep data or using AI models that are not capable of analyzing a patient's entire night of sleep. This new approach, developed using thousands of sleep recordings, takes a more comprehensive view. By training on full-length sleep data, the model can recognize sleep patterns throughout the night and across different populations and settings, offering a standardized and scalable method for sleep research and clinical use, say the investigators. "This is a step forward in AI-assisted sleep analysis and interpretation," says first author Benjamin Fox, a Ph.D. candidate at the Icahn School of Medicine at Mount Sinai in the Artificial Intelligence and Emerging Technologies Training Area. "By leveraging AI in this way, we can learn relevant clinical features directly from sleep study signal data and use them for sleep scoring and, in the future, other clinical applications such as detecting sleep apnea or assessing health risks linked to sleep quality." The model was built using a large dataset of sleep studies (polysomnograms) that measure key physiological signals, including brain activity, muscle tone, heart rate, and breathing patterns. Unlike traditional AI models, which analyze only short, 30-second segments, this new model considers the entire night of sleep, capturing more detailed and nuanced patterns. Further, the model is trained via a method known as self-supervision, which helps learn relevant clinical features from physiological signals without using human labeled outcomes. "Our findings suggest that AI could transform how we study and understand sleep," says co-senior corresponding author Ankit Parekh, Ph.D., Assistant Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai, and Director of the Sleep and Circadian Analysis Group at Mount Sinai. "Our next goal is to refine the technology for clinical applications, such as identifying sleep-related health risks more efficiently." The researchers emphasize that this AI tool, while promising, would not replace clinical expertise. Instead, it would serve as a powerful aid for sleep specialists, helping to speed up and standardize sleep analysis. Next, the team's research aims to expand its capabilities beyond sleep-stage classification to detecting sleep disorders and predicting health outcomes. "This AI-driven approach has the potential to revolutionize sleep research," says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, Director of the Hasso Plattner Institute for Digital Health, and the Irene and Dr. Arthur M. Fishberg Professor of Medicine. Dr. Nadkarni is also the inaugural Chief of the Division of Data-Driven and Digital Medicine and Co-Director of the Mount Sinai Clinical Intelligence Center. "By analyzing entire nights of sleep with greater consistency, we can uncover deeper insights into sleep health and its connection to overall well-being." The study's authors, as listed in the journal, are Benjamin Fox, Joy Jiang, Sajila Wickramaratne, Patricia Kovatch, Mayte Suarez-Farinas, Neomi A. Shah, Ankit Parekh, and Girish N. Nadkarni.

New AI Model Analyzes Full Night of Sleep With High Accuracy in Largest Study of Its Kind | Newswise

A transformer-based AI model analyzes eight-hour sleep signals from brain, movement, cardiac, and respiratory data to generate summaries, which are then used to classify sleep stages for the entire night. Newswise -- New York, NY [March 17, 2025] -- Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night's sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. Details on their findings were reported in the March 13 online issue of the journal Sleep [https://doi.org/10.1093/sleep/zsaf061]. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks. Current sleep analysis often relies on human experts manually scoring short segments of sleep data or using AI models that are not capable of analyzing a patient's entire night of sleep. This new approach, developed using thousands of sleep recordings, takes a more comprehensive view. By training on full-length sleep data, the model can recognize sleep patterns throughout the night and across different populations and settings, offering a standardized and scalable method for sleep research and clinical use, say the investigators. "This is a step forward in AI-assisted sleep analysis and interpretation," says first author Benjamin Fox, a PhD candidate at the Icahn School of Medicine at Mount Sinai in the Artificial Intelligence and Emerging Technologies Training Area. "By leveraging AI in this way, we can learn relevant clinical features directly from sleep study signal data and use them for sleep scoring and, in the future, other clinical applications such as detecting sleep apnea or assessing health risks linked to sleep quality." The model was built using a large dataset of sleep studies (polysomnograms) that measure key physiological signals, including brain activity, muscle tone, heart rate, and breathing patterns. Unlike traditional AI models, which analyze only short, 30-second segments, this new model considers the entire night of sleep, capturing more detailed and nuanced patterns. Further, the model is trained via a method known as self-supervision, which helps learn relevant clinical features from physiological signals without using human labeled outcomes. "Our findings suggest that AI could transform how we study and understand sleep," says co-senior corresponding author Ankit Parekh, PhD, Assistant Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai, and Director of the Sleep and Circadian Analysis Group at Mount Sinai. "Our next goal is to refine the technology for clinical applications, such as identifying sleep-related health risks more efficiently." The researchers emphasize that this AI tool, while promising, would not replace clinical expertise. Instead, it would serve as a powerful aid for sleep specialists, helping to speed up and standardize sleep analysis. Next, the team's research aims to expand its capabilities beyond sleep-stage classification to detecting sleep disorders and predicting health outcomes. "This AI-driven approach has the potential to revolutionize sleep research," says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, Director of the Hasso Plattner Institute for Digital Health, and the Irene and Dr. Arthur M. Fishberg Professor of Medicine. Dr. Nadkarni is also the inaugural Chief of the Division of Data-Driven and Digital Medicine and Co-Director of the Mount Sinai Clinical Intelligence Center. "By analyzing entire nights of sleep with greater consistency, we can uncover deeper insights into sleep health and its connection to overall well-being." The paper is titled "A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages." The study's authors, as listed in the journal, are Benjamin Fox, Joy Jiang, Sajila Wickramaratne, Patricia Kovatch, Mayte Suarez-Farinas, Neomi A. Shah, Ankit Parekh, and Girish N. Nadkarni. About Mount Sinai's Windreich Department of AI and Human Health Led by Girish N. Nadkarni, MD, MPH -- an international authority on the safe, effective, and ethical use of AI in health care -- Mount Sinai's Windreich Department of AI and Human Health is the first of its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health. The Department is committed to leveraging AI in a responsible, effective, equitable, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, leading-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice. The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai -- a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System -- which complements its mission by advancing data-driven approaches to improve patient care and health outcomes. At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale. In 2024, the Department's innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care. For more information on Mount Sinai's Windreich Department of AI and Human Health, visit: ai.mssm.edu About the Hasso Plattner Institute at Mount Sinai At the Hasso Plattner Institute for Digital Health at Mount Sinai, the tools of data science, biomedical and digital engineering, and medical expertise are used to improve and extend lives. The Institute represents a collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System. Under the leadership of Girish Nadkarni, MD, MPH, who directs the Institute, and Professor Lothar Wieler, a globally recognized expert in public health and digital transformation, they jointly oversee the partnership, driving innovations that positively impact patient lives while transforming how people think about personal health and health systems. The Hasso Plattner Institute for Digital Health at Mount Sinai receives generous support from the Hasso Plattner Foundation. Current research programs and machine learning efforts focus on improving the ability to diagnose and treat patients. About the Icahn School of Medicine at Mount Sinai The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the eight- member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to New York City's large and diverse patient population. The Icahn School of Medicine at Mount Sinai offers highly competitive MD, PhD, MD-PhD, and master's degree programs, with enrollment of more than 1,200 students. It has the largest graduate medical education program in the country, with more than 2,600 clinical residents and fellows training throughout the Health System. Its Graduate School of Biomedical Sciences offers 13 degree-granting programs, conducts innovative basic and translational research, and trains more than 500 postdoctoral research fellows. Ranked 11th nationwide in National Institutes of Health (NIH) funding, the Icahn School of Medicine at Mount Sinai is among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. More than 4,500 scientists, educators, and clinicians work within and across dozens of academic departments and multidisciplinary institutes with an emphasis on translational research and therapeutics. Through Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai. -------------------------------------------------------