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PhD student Narges Norouzi (left) and Professor Parham Aarabi (Both ECE).

A 42-year-old investment banker arrived at the emergency room with complaints of nausea, vomiting, anxiety and tremors. He told doctors he drank alcohol every day—often at business lunches—and at home every evening. Worried about his health, he decided to quit drinking and had his last Scotch 24 hours before going to the hospital.

It’s a common scenario in emergency rooms across North America: a patient suddenly stops regular, excessive alcohol consumption and experiences withdrawal, a potentially fatal, easily treatable side-effect.

The most common clinical sign of withdrawal is tremor, especially in the hands and arms. Judging tremor severity is harder than it sounds—it requires considerable medical expertise, and even experienced doctors’ estimates can vary widely. Chronic alcohol abusers often come to the emergency department claiming to be in withdrawal in an effort to obtain benzodiazepines—a class of sedatives used to treat alcohol withdrawal, anxiety and more—and it can be difficult for inexperienced clinicians to determine if the patient is actually in withdrawal or “faking” a tremor to get access to these prescription medications. Front-line healthcare workers had no objective way to tell the sufferers from the fakers—until now.

PhD student Narges Norouzi (left) and Professor Parham Aarabi (Both ECE).

Professor Parham Aarabi (ECE) teamed up with PhD candidate Narges Norouzi (ECE MSc 1T4) and Professor Bjug Borgundvaag of the Faculty of Medicine to develop the world’s first mobile app to measure tremor strength, providing objective guidance that can help direct treatment decisions. The app also shows promise in making solid predictions about whether the tremor is real or fake. [Watch a video of the app in action].

To obtain data, users hold an iPod in both hands for 20 seconds while the device’s built-in accelerometer measures the frequency of the tremor. Researchers tested the app on 49 patients experiencing tremors in the emergency room, as well as 12 nurses trying to mimic the symptom. The app showed significant results, with only 17 per cent of nurses able to “fake it.”

While studies were promising, Norouzi found that her app’s ability to assess tremor strength matched that of junior physicians, while more senior doctors were able to judge symptoms with better accuracy. Norouzi’s next move is to continue honing the tool, comparing its performance to doctors’ subjective assessments, and to further study the effects of left- or right-handedness.

“There’s so much work to do in this field,” said Norouzi. “There is other work out there on Parkinson’s tremors, but much less on tremors from alcohol withdrawal.”

The Tremor app quantifies the frequency of shaking in the patient’s left and right hand.

“The exciting thing about our app is that the implications are global,” said Professor Borgundvaag, who is also an emergency physician at the Schwartz/Reisman Emergency Centre at Mount Sinai Hospital. “Alcohol-related illness is commonly encountered, not only in the emergency room, but also elsewhere in the hospital, and this gives clinicians a much easier way to assess patients using real data.”

“Our app may also be useful in assisting withdrawal management staff, who typically have no clinical training in determining which patients should be transferred to the emergency department for medical treatment or assessment. We think our app has great potential to improve treatment for these patients overall.”

“We have just begun to scratch the surface of what is possible by applying signal processing and machine learning to body-connected sensors,” said Professor Aarabi. “As sensors improve and algorithms become smarter, there’s a good chance that we may be able to solve more medical problems and make medical diagnosis more efficient.”

Norouzi and the team presented this work on Aug. 29, 2014 at the International Conference of the IEEE Engineering in Medicine and Biology Society in Chicago.

Read more about this story in The Toronto Star.

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