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It’s a common scenario in emergency rooms across Canada: a patient suddenly stops regular, excessive alcohol consumption and develops withdrawal – a potentially fatal condition.

The most common clinical sign of withdrawal is tremor, especially in the hands and arms. But judging tremor severity is harder than it sounds; it requires considerable medical expertise and even experienced doctors’ estimates can vary widely.

To assist physicians in determining the severity of a patients withdrawal, researchers from the University of Toronto have developed the world’s first app to measure tremor strength, providing objective guidance to direct treatment decisions. The app also shows promise in making solid predictions about whether the tremor is real or fake.

“There’s so much work to do in this field,” said Narges Norouzi, a PhD candidate in the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE). “There is other work out there on Parkinson’s tremors, but much less on tremors from alcohol withdrawal.”

Although withdrawal is a potentially fatal condition, physicians are often reluctant to prescribe benzodiazepines – a class of sedatives used to treat conditions such as alcohol withdrawal, anxiety, seizures and  insomnia. That’s because they’re frequently abused and can be dangerous when mixed with other drugs, especially alcohol and opiates.

“The exciting thing about our app is that the implications are global,” said Bjug Borgundvaag, a professor at �鶹ֱ��app's Faculty of Medicine and 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,” he added.

Experts say chronic alcohol abusers often go to the emergency department claiming to be in withdrawal in an effort to obtain benzodiazepines and it can be difficult for inexperienced clinicians to determine if the patient is actually in withdrawal or “faking” a withdrawal tremor. Front-line healthcare workers have no objective way to tell the sufferers from the fakers. But researchers hope to change that.

"Our app may also be useful in assisting withdrawal management staff, who typically have no clinical training, and 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," said Borgundvaag.

Researchers tested their app on 49 patients experiencing tremors in the emergency rooms at Toronto’s Schwartz/Reisman Emergency Medicine Institute at Mount Sinai Hospital, St. Michael’s Hospital and Women’s College Hospital, as well as 12 nurses trying to mimic the symptom.

Their study shows that three-quarters of patients with genuine symptoms had tremors with an average peak frequency higher than seven cycles per second. Only 17 per cent of nurses trying to “fake” a withdrawal tremor were able to produce a tremor with the same characteristics, suggesting that this may be a reasonable cut-off for discriminating real from fake. The app uses data from an iPod’s built-in accelerometer to measure the frequency of tremor for both hands for 20 seconds.

In the emergency room, clinicians filmed their patients’ hand tremors while using the app and showed the footage to doctors afterward. 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 and comparing its performance to doctors’ subjective assessments, and to further study the effects of left- or right-handedness.

“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 Parham Aarabi of ECE. “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 August 29, 2014 at the International Conference of the IEEE Engineering in Medicine and Biology Society in Chicago.

Marit Mitchell is a writer with the Faculty of Applied Science & Engineering at the University of Toronto.