Detecting Parkinson’s Disease With LabVIEW and USB DAQ
"We selected NI on the strength of its products and its ability to understand the customers’ requirements."
- Michael Linderman,
Detecting Parkinson’s disease and other neurodegenerative diseases in their early stages.
Building a device with NI LabVIEW system design software and USB DAQ devices that records surface electromyogram (EMG) signals from hand muscles during handwriting.
Michael Linderman - Norconnect, Inc.
Each year, more than 50,000 Americans are diagnosed with Parkinson’s disease, a disorder of the brain that leads to shaking and difficulty with movement and coordination. However, detection can be complex, especially in early stages of the disease. We set out to build a system that could record EMG activity from hand muscles during handwriting. The results could then be used to diagnose Parkinson’s disease and other neurodegenerative issues such as tremors. We received support from the National Science Foundation’s Small Business Innovation Research program and from National Instruments. We also collaborated with several universities including Stanford University, St. Lawrence University, and Dartmouth Medical School during our research.
The subjects attach gel surface EMG electrodes to predetermined locations on their hands and put on gloves to hold the electrodes in place. Then they write on a tablet for a predetermined period of time. The data is collected from both the tablet and from the surface EMG electrodes using an NI USB-6008 digitizer. The analytical program, built in LabVIEW, evaluates muscle activity during this controlled set of movements.
We decided to try the system on three groups. We recruited six healthy young subjects, six healthy elderly subjects, and seven elderly patients with diagnosed Parkinson’s disease. Our hypothesis was that temporal interactions between hand muscle groups would deteriorate during handwriting in patients with Parkinson’s disease. Each subject wrote the number 3 approximately 400 times. We used the ensemble statistics of these handwriting trials to evaluate the connectivity between muscle activities in different times.
The graphs below show the difference in the image of handwriting electromyography between healthy and affected subjects.
- Healthy control
2. Parkinson’s patient
We concluded that analysis of EMG signals recorded during handwriting may serve as a simple tool to monitor and diagnose Parkinson’s disease. The reduction in correlations between muscle groups during handwriting may reflect the general deterioration in the coordination of motor activities, indicative of the disease.
We selected NI on the strength of its products and its ability to understand the customers’ requirements. After we had promising results with our old system, which consisted of very large and expensive off-the-shelf amplifiers and digitizer, and also included a separate attachment to a computer for writing where users could not see what they had written, we contacted our local NI field sales engineer and asked him to help us improve our technology. We were impressed by his hands-on experience and enormous theoretical knowledge, which is a rare combination. Demonstrating the power of LabVIEW as a software development environment, he developed a GUI for our data collection system within minutes. He also explained how we could use NI products for future product development.
Then we further developed our system and, with the new results, started to work on our methodology for analysis of biological signals. Our NI field sales engineer was closely involved in our projects and provided valuable advice. At one point, he even lent us an NI device so that we could study the integration between NI technology and other products. He helped us select the USB-6008 for our product based on our specifications, and advised us on various issues of integration with our amplifiers.
Using LabVIEW and USB DAQ, we successfully developed a new methodology to analyze neuronal activity. The main difference between our system and existing detection systems is the software program we use to statistically analyze EMG signals during handwriting. Currently, our device can analyze not only Parkinson’s disease, but also tremors.
The system is now used at Dartmouth-Hitchcock Medical Center, Claxton-Hepburn Medical Center, and the St. Lawrence University athletic department. We also recently presented our results at the 11th International Conference on Alzheimer’s and Parkinson’s Diseases (AD/PD 2013) in Florence, Italy. As a result, we made many contacts with other researchers who are interested in using our device and methodology in their clinical studies. We also received a Rapid Response Innovation Award grant from the Michael J. Fox Foundation to continue this research.
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