Amazing stuff!
"Difficulty sleeping often precedes heart disease, psychiatric disorders, and many other illnesses. Researchers used data gathered during sleep studies to detect such conditions.
What’s new: SleepFM is a system that classifies Alzheimer’s, Parkinson’s, prostate cancer, stroke, congestive heart failure, and many other conditions based on a person’s vital signs while asleep — as much as 6 years before they show symptoms. ...
Input/output: Recordings of one night of sleep in, disease classifications out
Architecture: Convolutional neural network encoder, transformer, LSTM
Performance: Can accurately classify over 130 conditions ...
How it works: SleepFM comprises a convolutional neural network (CNN), transformer, and LSTM. The authors trained the system in two stages:
(i) to encode patterns in sleep data and
(ii) to classify diseases.
The training data comprised roughly 585,000 hours of sleep-study recordings that included, in addition to each patient’s age and sex, signals of activity in the brain, heart, respiratory system (airflow, snoring, and blood oxygen level), and leg muscles. The data was mostly proprietary but included public datasets.
The authors trained the CNN and transformer together. ...
The authors added the LSTM and separately trained it, given 9 hours of sleep data as well as the subject’s age and sex, to classify more than 1,000 diseases.
Results: The authors compared SleepFM’s performance on a proprietary test set to the same system without pretraining and a vanilla neural network that was trained on only demographic information.
Across 14 general categories of disease ..."
From the abstract:
"Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization, generalizability and multimodal integration.
To address these challenges, we developed SleepFM, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations.
Trained on a curated dataset of over 585,000 hours of PSG recordings from approximately 65,000 participants across several cohorts, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk.
From one night of sleep, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected P < 0.01), including all-cause mortality (C-Index, 0.84), dementia (0.85), myocardial infarction (0.81), heart failure (0.80), chronic kidney disease (0.79), stroke (0.78) and atrial fibrillation (0.78).
Moreover, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study—a dataset that was excluded from pretraining—and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks, achieving mean F1 scores of 0.70–0.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence.
This work shows that foundation models can learn the language of sleep from multimodal sleep recordings, enabling scalable, label-efficient analysis and disease prediction."
New AI model predicts disease risk while you sleep "Stanford Medicine scientists and their colleagues created the first artificial intelligence model that can predict more than 100 health conditions from one night’s sleep."
Fig. 1: Overview of SleepFM framework.
No comments:
Post a Comment