Patent Document ID: 8204583
Application ID: 12288834
Patent Flag: 1

Claim One:
1. A system for monitoring brain pathological conditions and detecting critical neurological events of a patient in real time from a series of analyses on scalp or intracranial brain electrical activity, such as multi-channel EEG signals, comprising: a. an amplifier configured to receive and amplify the EEG signals; b. an analog-to-digital multi-converter configured to convert the EEG signals into digital signals; c. a signal processor configured to process the digital signals and to provide outputs indicative of the brain pathological conditions, wherein the processor is programmed to perform analyses for each EEG channel as follows: 1.c-1. filter signals in a first frequency range A and a second frequency range B, each frequency range including a plurality of segments; 1.c-2. for each segment in the first frequency range A, calculate nonlinear EEG descriptors that extract signal characteristics, wherein the nonlinear EEG descriptors include short-term maximum Lyapunov exponent (STLmax) and pattern match regularity statistic (PMRS-A) and additionally include descriptors selected from the group consisting of correlation dimension, entropy, mutual information and combinations thereof; 1.c-3. calculate a convergence measure of the nonlinear EEG descriptors among selected EEG channels for each sequential segment; 1.c-4. calculate a seizure susceptibility index (SSI) based on a distribution of STLmax, and PMRS as well as respective convergence measures T-index 1 and T-index 2 outputs; 1.c-5. determine whether an impending seizure is susceptible based on SSI values; and 1.c-6. when the impending seizure is indicated, perform the following: i) for each segment in the second frequency range B, calculate a pattern match regularity statistic (PMRS-B); ii) for each segment in the second frequency range B, calculate the standard deviation of the signal amplitude (STD); iii) for each segment in the second frequency range B, calculate a maximum and minimum one second standard deviation of a signal amplitude (STX and STM); iv) for each segment in the second frequency range B, calculate a maximum one-second negative-to-positive zero-crossing frequency, where each one second signal is normalized to have mean zero (ZCmax); v) for sequential EEG segments, calculate the maximum amplitude difference between a low and a high frequency band (DAmp); and vi) determine whether a seizure event is occurring based on PMRS-B, STD, STX, STM, DAmp, and ZCmax outputs.