Indian-Origin Neurologist Develops New Seizure Prediction Method for Epilepsy Patients
Friday, 19 July 2024, 16:31 IST
Dr. Vikrama Rao, an Indian-origin neurologist at the University of California-San Francisco (UCSF), has developed an innovative method for predicting seizures in epilepsy patients. This breakthrough could significantly improve the quality of life for millions grappling with the uncertainty of epilepsy.
Current seizure prediction methods often involve prolonged data collection and may lack accuracy. While implanted responsive neurostimulation systems (RNS) monitor brain activity and aim to prevent seizures through electrical stimulation, they sometimes respond too late.
Dr. Rao’s team has pioneered a technology that predicts seizures by analyzing brain communication patterns. The researchers studied 15 volunteers with hippocampal seizures using RNS devices and identified a multi-day activity cycle associated with seizures. This cycle, characterized by increased synchronization between the hippocampi, allows for seizure prediction up to a day in advance.
The team’s algorithm, which processes just 90 seconds of data, successfully forecasted seizure risk within the next 24 hours. This method, termed "snapshot seizure forecasting," proved effective for nearly all participants, enabling individuals to take preventive measures.
Plans are underway to test this technology on a larger scale and develop noninvasive data collection methods. Surveys suggest that 70% of epilepsy patients would embrace such a forecasting system to better manage their daily lives, reducing the fear of unpredictable seizures and enhancing overall quality of life.
Current seizure prediction methods often involve prolonged data collection and may lack accuracy. While implanted responsive neurostimulation systems (RNS) monitor brain activity and aim to prevent seizures through electrical stimulation, they sometimes respond too late.
Dr. Rao’s team has pioneered a technology that predicts seizures by analyzing brain communication patterns. The researchers studied 15 volunteers with hippocampal seizures using RNS devices and identified a multi-day activity cycle associated with seizures. This cycle, characterized by increased synchronization between the hippocampi, allows for seizure prediction up to a day in advance.
The team’s algorithm, which processes just 90 seconds of data, successfully forecasted seizure risk within the next 24 hours. This method, termed "snapshot seizure forecasting," proved effective for nearly all participants, enabling individuals to take preventive measures.
Plans are underway to test this technology on a larger scale and develop noninvasive data collection methods. Surveys suggest that 70% of epilepsy patients would embrace such a forecasting system to better manage their daily lives, reducing the fear of unpredictable seizures and enhancing overall quality of life.
