Detecting Patients' Pain Levels Via Brain Signals

The system could help doctors diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery.

Pain management is a surprisingly challenging, complex balancing act. Overtreating pain, for example, runs the risk of addicting patients to pain medication. Undertreating pain, on the other hand, may lead to long-term chronic pain and other complications. Today, doctors generally gauge pain levels according to their patients' own reports of how they're feeling. But what about patients who can't communicate how they're feeling effectively—or at all—such as children, elderly patients with dementia, or those undergoing surgery?

In a paper presented at the International Conference on Affective Computing and Intelligent Interaction, the researchers describe a method to quantify pain in patients. To do so, they leverage an emerging neuroimaging technique called functional near infrared spectroscopy (fNIRS), in which sensors placed around the head measure oxygenated hemoglobin concentrations that indicate neuron activity.

For their work, the researchers use only a few fNIRS sensors on a patient's forehead to measure activity in the prefrontal cortex, which plays a major role in pain processing. Using the measured brain signals, the researchers developed personalized machine-learning models to detect patterns of oxygenated hemoglobin levels associated with pain responses. When the sensors are in place, the models can detect whether a patient is experiencing pain with around 87 percent accuracy.

"The way we measure pain hasn't changed over the years," says Daniel Lopez-Martinez, a Ph.D. student in the Harvard-MIT Program in Health Sciences and Technology and a researcher at the MIT Media Lab. "If we don't have metrics for how much pain someone experiences, treating pain and running clinical trials becomes challenging. The motivation is to quantify pain in an objective manner that doesn't require the cooperation of the patient, such as when a patient is unconscious during surgery."

Traditionally, surgery patients receive anesthesia and medication based on their age, weight, previous diseases, and other factors. If they don't move and their heart rate remains stable, they're considered fine. But the brain may still be processing pain signals while they're unconscious, which can lead to increased postoperative pain and long-term chronic pain. The researchers' system could provide surgeons with real-time information about an unconscious patient's pain levels, so they can adjust anesthesia and medication dosages accordingly to stop those pain signals.

Joining Lopez-Martinez on the paper are: Ke Peng of Harvard Medical School, Boston Children's Hospital, and the CHUM Research Centre in Montreal; Arielle Lee and David Borsook, both of Harvard Medical School, Boston Children's Hospital, and Massachusetts General Hospital; and Rosalind Picard, a professor of media arts and sciences and director of affective computing research in the Media Lab.

Medical Express
To read this article in its entirety CLICK HERE.