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. Rahul Thapa and Magnus Ruud Kjaer worked with colleagues at Stanford University, Danish Center for Sleep Medicine, Technical University of Denmark, BioSerenity, Harvard Medical School, and University of Copenhagen.
- 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, including experiencing congestive heart failure or stroke within six years.
- Availability: Weights, training code, and inference code are available for download for commercial and noncommercial uses. Part of the dataset is available for noncommercial use.
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. Given 5 minutes of recordings, the CNN learned to produce embeddings of each type of signal, while the transformer modified the embeddings to capture relationships within a signal type across time. The CNN and transformer were encouraged to produce similar embeddings of sleep recordings that were made at the same time and different embeddings of recordings that weren’t.
- 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, SleepFM achieved a higher area under the curve (AUC), a measure of true versus false positives (positive meaning the condition occurred within six years of the recording), higher is better. For example, classifying post-traumatic stress disorder, SleepFM achieved 0.75 AUC, while the same system without pretraining achieved 0.64 AUC.
- Predicting Atrial Fibrillation in the public sleep dataset SHHS, SleepFM achieved 0.81 AUC, while earlier work trained solely for that purpose achieved 0.82 AUC.
Why it matters: AI’s ability to recognize subtle patterns has amazing potential in medicine and beyond. In this application, it could provide early warning of serious diseases, enabling people to take steps to prevent illness before it develops.
We’re thinking: We’re wide awake after reading this paper!