Parkinson’s Test Device DevelopmentTiffany Feltman1 and Erin Sikkel1

Advisors: Dr. Paul King1, Dr. Peter Konrad2, Judith Dexheimer3, Randy Carnevale3

Department of Biomedical Engineering1, Director of Functional Neurological Surgery2, Department of Bioinformatics, Vanderbilt University, Nashville, TN

Problem StatementDr. Peter Konrad, the Director of Functional Neurological Surgery at Vanderbilt University, has stated that the overall goal of the project is to try and quantify ways in which Parkinson’s patients respond to stimulation therapy. We are striving to create a software program for use with a laptop computer that a doctor could bring to the clinic or operating room in which a patient uses a joy-stick to track a moving object on the screen and the program measures the time it takes to follow that object around the screen. We would also like to be able to output the timed response in some sort of excel file. We are working with two Vanderbilt University graduate students, Judith Dexheimer and Randy Carnevale, Biomedical Informatics students at Vanderbilt University, who are creating the software that will be used. Our specific goal for this project is to create a device that is easier for the patients to use and have it be compatible with the software. The problems with current devices are that patients with Parkinson’s have trouble holding onto the joystick, due to their tremors and their inability to close around objects securely. Also, since many of the suffering from Parkinson’s are elderly, understanding how to use a joystick is a relatively foreign task

Parkinson’s Disease: A Closer Look

Current Diagnoses Process

Design Description

Cost to Manufacture

Acknowledgements

• Have a device that is easy to use for those affected by motor disorders as current devices are too hard to use - Needs an easy to hold knob - Have a way to hold it down to the table - Remove resistance so that it does not return to zero• Have a device that will be compatible with the software

Ideal Design

According to the National Parkinson’s Disease Foundation, Parkinson’s disease affects both men and women. In the United States, there are roughly 60,000 new cases diagnosed each year, which is in addition to the 1.5 million Americans who already have Parkinson disease. Most people are usually affected after the age of 65, but roughly 15% are diagnosed under the age of 50. Parkinson’s disease is the second most common neurodegenerative disease after Alzheimer disease. Eventually, dementia occurs in about 20% of patients with Parkinson’s, making one common relationship between that and Alzheimer disease. Parkinson’s disease is a disorder of the brain and occurs when the neurons in the substantia nigra die or become slightly impaired. These neurons produce the chemical dopamine which allows for smooth, coordinated functioning of the muscles in the body. In Parkinson’s disease, due to the neurodegeneration, symptoms such as tremors, slowness of movement, rigidity, and difficulty with balance, may appear. These problems can lead to a deeper state of the disease which includes small handwriting, stiff facial expression, shuffling walk, muffled speech, which can inherently lead to depression. The loss of dopamine results in abnormal nerve firing patterns within the brain causing impaired movement.

Currently, there is no one way to diagnose patients suffering from Parkinson’s disease, leading to conflicting diagnoses. A doctor performs a neurological exam consisting of testing reflexes, muscle strength, coordination, balance, and details of movement, which is also used to rule out other conditions with similar symptoms. The only way to officially know that a patient suffers from Parkinson’s disease, is to perform an autopsy.

Pet Scan of a Normal brain (left) and a Parkinson’s brain (right) Degeneration of the substansia nigra. Parkinson’s patient (left), Normal patient (right)

Dr. Paul King, Department of Biomedical Engineering, Dr. Peter Konrad, Department of Functional Neurological Surgery, Judith Dexheimer and Randy Carnevale, Department of Bioinformatics, Vanderbilt University, Nashville, TN. Will Durfee, Department of Mechanical Engineering, University of Minnesota.

The joystick we wanted to design needed to have a manageable handle for easy movement and grasp, must be compatible with the computer program, be able to move in certain directions to a certain degree, and be able to have continuous movement to a certain range. To begin the process, we looked into the different joysticks available and the specifications of each one. The current joysticks are hard for the patient to use due to their motor disorders and the problems they have with tremors. The joystick we picked was the Atari 2600 model CX40. This was one of the first joysticks released and we picked it because of its simplicity. It only has two buttons on it, which makes it easy to manipulate and the computer program did not need more then one button. After it was purchased, we wanted a joystick that the patient would feel comfortable using and was easy for the patient to use. We looked into the different designs that were available for a car gear shift, because they are very close to the design we wanted to use for our joystick. After looking into these designs, we came to the conclusion that they were too small for the hand to rest with ease. When we were thinking of different balls or knobs that are bigger than these shifters, we came up with the idea of using a softball. This idea was then put into motion by first drilling a hole into the softball using a drill bit that was designed to make a large hole. This hole was the exact size of the top of our Atari joystick. The joystick was then fixed to where the button would stay pressed down because it would be easiest on the patient. The patient would then just have to move the joystick without worrying about buttons and how they were going to be able to work it. This was done by taping the buttons down so that they would always be working and then the softball was placed on top of the joystick so that it was just a big ball for the patient to work with.

We would also have to include the software that we have used in the package, but have not come up with a price for that. Since it would be used in the medical field, and would be at such a high demand, it would be very probably that the software and device together could be sold for a good amount. This is the most variable part of the pricing for the project, as we are not familiar with pricing of computer programs. It would depend on the company who would sell it to determine the price.

Conclusion

Data

Picture of actual device in progress

Distance between target and response in x-direction

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86

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96

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61

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82

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93

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Control

Distance between target and response in y-direction

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Comparisons between the target and response in the x and y movements compared between a Parkinson’s patient and Controls and their corresponding data

Accuracy Index

0.93

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Parkinso

npt 1 pt 4 pt 5 pt 6 pt 7 pt 8 pt 9

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AI (X) AI (y)

Table showing the comparison of the accuracy index between a Parkinson’s patient and Controls in the x and y direction

Data was collected using a Visual Basics program created by Dr. Will Durfee from the University of Minnesota. The program was converted to Matlab and run to collect data comparing the distances between the targets and responses in both the x and y direction showing a comparison between the Parkinson’s patient and the Controls. An accuracy index was also collected for all subjects and compared.

From the data obtained in the distance calculation, a student’s t-test was performed to determine the significance and to determine whether to reject or accept the null hypothesis. The p-value that returned from the correlation between the x-distance was calculated to be 2.87902 x 10-24 while the p-value for the correlation between the y-distance was calculated to be 1.16652 x 10-28. A small p-value suggests that the null hypothesis is unlikely to be true. In this case, the null hypothesis was that the distance the patient performed would be the same for the control and the Parkinson patient and there is no statistical significant difference between them. The smaller the p-value, the more convincing the argument for the rejection of the null hypothesis will be. Thus, in our design, the null hypothesis is rejected for both directions due to the p-value being so small. This says that there is a difference between the performances of both patients and that there is a statistically significant difference between them. However, since the design team was only able to test such a small amount of patients, more data should be received before it is precisely stated.

Discussion

The computer program and joystick were tested in Dr. Konrad’s office to evaluate the way the computer program works and to validate our outputs. From our outputs we hoped to find a more definite way to quantify Parkinson’s disease. In our study, 13 patients were evaluated; 12 of them were normal and 1 was an afflicted patient. From close evaluation of the data obtained for each patient, 2 of the patient’s data were not used. These were patients 2 and 3. The data was thrown out because the computer program does not yet have the ability to start and stop at the same distance, so after comparing them with all the trials, their test values were the only ones with different target distances. After the 13 patients had completed all 4 tasks, the output was sent into an excel file from the text file it was presented in. The output excel file contains all of the target and response distances in the x and y direction and the time during the task. The excel file was then saved in the Matlab workspace and imported into Matlab through the import wizard. The variables in both the code and the excel file had to be the same for correlation reasons. The M-file code was then implemented and returned all the variables one hoped for. It returned the E (RMS), CE, VE, P, and AI. All of the data returned from the Matlab program was then placed into excel to finalize the evaluation of the data. The Accuracy Index was graphed for each patient in each direction. The Accuracy Index, as one can see from the graph, is similar for all the patients but the Parkinson patient. This difference is a very small one, but it does show that the computer program and joystick are capable of deciphering between normal and diseased.