EEG Monitors and Quantifies Brainwave Activity of a Patient
This ambulatory brain state advisory system determines and displays current brain states of a patient for more effective treatment. Electroencephalogram (EEG) is a valuable tool for research and diagnosis and are used to diagnose and monitor epilepsy, sleep disorders, comas, and the depth of amnesia in patients. In the United States, more than 40 million adults suffer from sleep disorders while 3 million suffer from epilepsy. University of Florida researchers have created an ambulatory system to monitor brain-wave activity, classifying existing conditions for the patient, allowing for more effective treatment than what is available. The EEG brain state advisor is patient-specific, adjusting the basis of the EEG decomposition to each patient’s brain activity, which improves performance, and allows for the estimation of a given condition. The advisory system output can be displayed on a portable, handheld device or a watch-form device, making it adaptable for ambulatory uses.
Monitors and quantifies the current brain state of a patient
- Displays on portable or watch-form device, making the system applicable in ambulatory conditions
- Adjusts to each patient, achieving diagnostic sensitivity and specificity
- Quantifies brain states in health and disease states, allowing varied use of the system
EEGs are used by physicians to test electrical activity in the brain of a patient for medical evaluation. Researchers at the University of Florida have created a system for monitoring brainwave activity that automatically calibrates to the patient, thus acting as a patient-specific brain state advisor. First, the system uses time-series decomposition that corresponds to a model of the patient’s brain activity, describing the collected brainwaves in terms of phasic events. These phasic events are then used within a framework to project points to separate abnormal and normal brain states, or brain states that correspond to different stimuli. Finally, the projection is used to create a probabilistic model for each clinical diagnostic condition of interest. This works by using landmark points, in which the distance to a landmark point for a certain condition would predict the likelihood of that condition. The past and current brain states are displayed alongside the landmark points, providing a patient-specific brain state advisory.