Detecting Parkinson’s Through Nighttime Breathing Patterns
Researchers have made a significant discovery by analyzing nighttime breathing patterns to detect Parkinson’s disease. This approach could transform how the disease is diagnosed and monitored, offering earlier detection and improved care for patients.
Key Findings
Breathing patterns during sleep were used to identify Parkinson’s with remarkable accuracy.
The method also tracked changes in symptoms over time, providing a reliable way to monitor progression.
Parkinson’s disease is a progressive condition that affects movement and coordination due to the loss of dopamine-producing brain cells. Diagnosing it can be a slow process, as there are no straightforward tests like blood work or imaging. This delay often leaves patients waiting years for answers, delaying necessary care and treatment.
The Study
A team led by Dr. Dina Katabi at MIT explored how breathing patterns during sleep could provide early indications of Parkinson’s. Using two methods to collect data, the study monitored participants in two ways:
Breathing Belts: These devices track chest movement while the participant sleeps.
Wireless Sensors: Positioned near the bed, these sensors use radio waves to measure breathing without physical contact.
With data from more than 7,600 individuals—757 of whom had Parkinson’s—the findings were compelling:
Using a single night of data from breathing belts, the system identified Parkinson’s cases with 80% accuracy.
The accuracy increased to 86% when using wireless sensor data for one night and reached 95% when analyzing data from 12 nights.
Early Detection and Monitoring
One remarkable finding was the program’s ability to detect Parkinson’s in individuals who had undergone sleep studies years before their official diagnosis. In these cases, it correctly identified three-quarters of the participants as having signs of the disease. This ability to detect Parkinson’s early could significantly improve outcomes by enabling earlier intervention.
The method also proved effective at tracking the progression of symptoms. Unlike current clinical scales, which can be inconsistent and subjective, this approach reliably detected subtle changes over time.
Future Potential
This research highlights an opportunity to make Parkinson’s care more accessible. Wireless sensors, in particular, offer a non-invasive and practical solution for individuals who may struggle to access traditional diagnostic tools. Dr. Katabi noted, “This method has the potential to reach underserved communities and bring Parkinson’s care closer to home.”
Conclusion
By identifying Parkinson’s through something as routine as breathing during sleep, this research offers a promising path forward. If further validated in diverse populations, it could accelerate the development of treatments, provide earlier diagnoses, and improve care for patients worldwide.
Citation: Yang Y, Yuan Y, Zhang G, Wang H, Chen YC, Liu Y, Tarolli CG, Crepeau D, Bukartyk J, Junna MR, Videnovic A, Ellis TD, Lipford MC, Dorsey R, Katabi D. Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals. Nat Med. 2022 Oct;28(10):2207-2215. doi: 10.1038/s41591-022-01932-x. Epub 2022 Aug 22. PMID: 35995955; PMCID: PMC9556299.