Professors at the University of Iowa are looking to use a computer algorithm to detect the early stages of Parkinson’s disease, a debilitating neurological disorder that results in tremors and other neurological symptoms.

The study received over $1.8 million in funding from the National Institute of Health’s National Institute on Aging. The disease currently affects about one million Americans, with no cure currently and limited treatments to manage the disease’s progression.

Currently, diagnosing Parkinson’s can take months of intensive interviews with patients, and Parkinson’s experts are few and far between in rural areas.

Soura Dasgupta, a UI professor and one of the principal investigators on the project, said people who live in rural areas may not have access to these diagnosis methods, leading to late diagnoses or no diagnosis at all.

One of the researchers’ new methods to diagnose Parkinson’s involves an electroencephalogram (EEG), a medical device that detects brainwaves through electrodes placed on a patient’s head. EEGs are prevalent in most medical centers around the country.

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The research team of UI professors has been working on developing a less complex algorithm to use with an EEG to not only diagnose Parkinson’s but to track the disorder’s progress over time within patients.

Nandakumar Narayanan, a UI professor of neurology, originally had the idea to use an EEG when he observed something interesting while working with patients.

“So as a neurologist, I’ve noticed that my patients with dementia have abnormal brain waves and I wondered if there would be a way to use my observation, that patients have abnormal brain rhythms, for diagnosis and following patients,” Narayanan said. “And that’s when I turned to Dasgupta.”

Dasgupta is a professor at the College of Engineering, his main role in the study involves developing the actual algorithm for the EEG.

This algorithm cuts down the time of diagnosis to five to six minutes rather than other methods, like lumbar taps or questionnaires, which can take hours and be expensive.

The algorithm also allows the creation of a “biomarker” so the team can track the patient’s progress and predict things.

Ergun Uc, the director of the UI Division of Movement Disorders and another UI professor of neurology, elaborated on the importance of the EEG.

“We lack feasible, accurate diagnostic and predictive markers, so here the EEG as a technique has been around for many decades, and also this particular technique does not require any special EEG, it only relies on resting EEG [data],” Uc said.

One factor that made the study so effective was its geographic location, Dasgupta said. He noted that the UI is uniquely situated with its access to UI Hospitals and Clinics, which has resources that allow for the collection of new EEG data, and a record of past recordings which can be used to correlate with the progress of patients.

Uc said the study received the federal grant because of the collaborations between the Colleges of Engineering and Medicine at the UI.

“There are great opportunities, also a very broad and deep expertise in many fields, so we should look for fruitful collaborations across the campus and keep working together to advance science, to have more patients,” Uc said.

The team will continue testing and development of the algorithm and has not yet finalized a release date for the technology.