Real-time detection of acute changes in neurophysiological state, such as epileptic seizures, lapses in cognitive ability, acute stress, etc., can ultimately serve to prevent accidents in high-risk occupations that require unwavering focus. Such professions include hazardous cargo trucking, heavy machinery operation, security and defense, air traffic control, etc. Indeed, technology for acquiring rich biosensor data streams that capture brain function, e.g., electroencephalography, are becoming increasingly portable and noninvasive. These developments present an opportunity for implementing not only real-time monitoring, but also providing pre-emptive alerts (e.g., smart phone displays), which can be used to indicate degradation in physiological states. This research has direct applications in biomedical settings - for instance, epilepsy, is one of the most common neurological disorders afflicting over 50 million people worldwide, including 3 million people in the U.S. In about 25 percent of these patients, epileptic seizures are not controlled using available medications. Being able to detect (or predict) the onset of epileptic seizures would significantly enhance the patient's quality of life. In a proof-of-concept study, the novel analytical approaches by the research team detected the onset of epileptic seizures within 2.5 seconds. In contrast, existing approaches have a detection delay exceeding 7 seconds. From a broader perspective, the findings of this research can transform the status quo in real-time monitoring of neurophysiological function. The multidisciplinary research team will strive to provide state-of-the-art research and training opportunities for a diverse group of students that bridges the gap from engineering to the life and brain sciences.
The research team will develop a sensor data fusion approach based on graph theoretic topological mapping to combine data acquired from multiple biosensors for neurophysiological change point detection. Unlike existing approaches, which rely on complex signal pre-processing, the graph theoretic approach eschews these computationally demanding steps and is therefore more viable in a practical setting. The research team will exploit this framework using a data library of high-resolution neurophysiological recordings acquired from end users in realistic settings that induce shifts in global functional states (e.g., acute stress, cognitive exhaustion, and fatigue and so on). The research team will integrate automated decision-making approaches in the overall schema to synthesize the information and provide easily interpretable feedback to the end user (e.g., displays on a smart device). Furthermore, the PIs will customize biosensors to accommodate the patient's lifestyle.
