AUTOMATED DETECTOR AND CLASSIFIER OF HIGH FREQUENCY OSCILLATIONS AND INDICATOR SEIZURE ONSET

摘要

High frequency oscillations (HFOs) are automatically detected in electroencephalogram (EEG) signals and analyzed to assess whether they are predictive of the onset of a neurological dysfunction in a subject or an indication of nonneurological electrical activity or noise in the EEG signal. In some examples, HFOs, serving as a biomarker for epileptic seizures, are identified and used to identify seizure networks within a patient for clinician monitoring or for controlling automated treatment systems. The analysis may be used to create enhanced EEG displays, with HFOs identified on the EEG.

主权项

1. A method comprising:
continuously receiving, at a signal processing device, neuronal electrical activity signal data taken from a plurality of electrodes and over a sampling window of time; forming, in the signal processing device, an optimized signal from the neuronal electrical activity signal data; identifying, in the signal processing device, windows of low-quality signal data collection within the optimized signal and removing the identified windows to form a quality-assured epochs of data collection; and from the optimized signal, detecting high frequency oscillations in the optimized signal and determining a rate and/or features of high frequency oscillations over the sampling window of time, wherein the rate and/or features of high frequency oscillations are predictive of the onset of a neurological dysfunction in a subject; and from the optimized signal with detected high frequency oscillations, identifying and displaying the time, location, rate and/or features of the high frequency oscillations within a clinical viewing platform such that a physician or caregiver can visualize this additional information and incorporate it into clinical decision making.