A SYSTEM developed by scientists at a Scottish university could help improve the quality of life for people with Parkinson’s disease.
Researchers at Heriot-Watt have found a way of detecting a condition caused as a side-effect of a treatment for the disease and it is hoped this can be used to target medication more accurately.
The motor features of Parkinson’s Disease, such as tremor, postural instability and a general slowing of movement, are caused by a lack of dopamine.
Clinicians treat this through replacement drugs such as levodopa, but prolonged exposure can lead to dyskinesia, causing involuntary jerking and spasms of the whole body.
About 90 per cent of patients treated with dopamine replacement drugs over 10 years report symptoms, but the exact cause of the condition is unknown. Now the Heriot-Watt team has created an algorithm which can detect the condition and have conducted clinical studies that prove their algorithm is reliable in picking it up.
Scientists are using their study to develop a new home-monitoring device for patients that will help clinicians adapt and improve their treatment.
Dr Michael Lones, right, associate professor of computer science at Heriot-Watt, said: “The problem is that as Parkinson’s disease worsens over time, the dose required to treat the motor features increases, which increases the risk of inducing dyskinesia, or making it more severe and prolonged for patients who already have it,”
“Patients don’t see their clinicians that frequently, and medication only changes at regular review periods so it’s very difficult for clinicians to know when dyskinesia is occurring.
“A better solution would be a portable device that identifies and monitors dyskinesia while patients are at home and going about day-to-day life, and broadcasts data to their clinicians through simple mobile technology.”
Lones and his team carried out two clin- ical studies, involving 23 Parkinson’s disease patients who had all displayed evidence of dyskinesia. Three trained clinicians then graded the intensity of the patients’ condition.
“The clinical studies allowed us to capture and mine data about how patients move and used those to build models,” Lones said.
“We developed our algorithm to make as few assumptions as possible. With traditional analysis, you make assumptions about what a movement looks like. If it doesn’t look like exactly that way, you won’t detect it.
“The algorithm works by building a mathematical equation that describes patterns of acceleration which are characteristic of dyskinesia.
“The system then uses this equation to discriminate periods of dyskinesia from other movements.
“This information is relayed to clinicians who can then adapt a patient’s medication as necessary.
“We’ve demonstrated that our system can reliably detect clinically significant dyskinesia, which is the information clinicians need to adjust a patient’s medication and more effectively manage the side-effects.
“These currently reduce the quality of life for a great number of patients.”
This research was done in collaboration with the University of York and with clinicians at the Leeds Teaching Hospitals NHS Trust.
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