This New Study Uses Night Breathing Patterns To Diagnose Parkinson’s Disease

Parkinson’s disease is a health problem caused by the damage or death of brain cells in the part, which produces dopamine. This chemical is responsible for smooth and purposeful movements. If the damage isn’t dealt with, it further causes unintended or uncontrollable movements. These include stiffness, shaking and difficulty with balance and coordination. The symptoms of the disease usually begin to worsen gradually. The condition can only be diagnosed based on certain symptoms as there is no way of diagnosing it through blood tests or imaging tests.

Currently, a group of researchers led by Dr. Dina Katabi from Massachusetts Institute of Technology have been testing ways to use artificial intelligence to diagnose Parkinson’s disease. The NIH-funded research has published a study that revealed that the group has designed a computer program based on neural networks that can analyze breathing patterns during sleep and arrive at the diagnosis of the disease. The areas of the brains that are affected first during Parkinson’s disease are the ones that control breathing and sleep.

The two types of tests used to monitor the patients were the one where a wireless sensor analyzed their data and the other where patients wore a belt on their chest (polysomnogram tests).

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The data included more than 7,600 people that included 757 Parkinson’s disease patients. The results came on August 22, 2022, and the program’s accuracy came out to be 80% from the first night’s data. The number rose to 86% when the program used a single night of wireless breathing data. By the end of 12 nights, the accuracy rose to 95%.

The team also tested the program’s capability of tracking the intensity of Parkinson’s disease. However, currently the program is insensitive to big changes. As compared to them, the program was more receptive to small changes caused by the disease.

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