Abstract: A method for determining an increased risk of death of a patient includes receiving ECG data of the patient generated during a first time period; receiving EEG data of the patient generated during the first time period; composing a feature of the ECG data and a feature of the EEG data over a common time frame and determining a statistical measure of association between the ECG data and the EEG data; and determining whether the degree of association exceeds a predetermined threshold, thereby indicating whether an increased risk is present.

Abstract: A method for determining an increased risk of death of a patient includes receiving ECG data of the patient generated during a first time period; receiving EEG data of the patient generated during the first time period; composing a feature of the ECG data and a feature of the EEG data over a common time frame and determining a statistical measure of association between the ECG data and the EEG data; and determining whether the degree of association exceeds a predetermined threshold, thereby indicating whether an increased risk is present.

Abstract: One aspect of the present disclosure relates to a system that can identify a focal area of abnormal brain interactions in a subject. Time series data can be received that corresponds to recordings from a plurality of regions in a brain of the subject during a resting period. Based on the time series data, an information inflow associated with each of the plurality of regions can be determined. The focal area of the abnormal brain interactions can be identified as one of the plurality of regions having a maximum information inflow.

Abstract: One aspect of the present disclosure relates to a system that can identify a focal area of abnormal brain interactions in a subject. Time series data can be received that corresponds to recordings from a plurality of regions in a brain of the subject during a resting period. Based on the time series data, an information inflow associated with each of the plurality of regions can be determined. The focal area of the abnormal brain interactions can be identified as one of the plurality of regions having a maximum information inflow.

Abstract: For analyzing a multi-component system, a method acquires a plurality of signals, each having a different spatial location of the multi-component system, and generates dynamic profiles for each of the plurality of signals. Each of the plurality of dynamic profiles reflects dynamic characteristics of the corresponding signal in accordance with each one of a plurality of dynamic measures. The method selects pairs of dynamic profiles from the acquired dynamic profiles based on a predetermined level of synchronization and generates a statistical measure for each of the selected plurality of pairs of dynamic profiles. The method characterizes state dynamics of the multi-component system as a function of at least one of the generated statistical measures, and generates a signal indicative of the characterized state dynamics of the multi-component system.

Abstract: An exemplary method for analyzing behavior of a system includes receiving dynamical measurement data regarding the system, applying a quadratically constrained quadratic 0-1 problem to identify data among the received data, and storing the identified data. An exemplary seizure warning method includes continuously calculating STLmax values, identifying critical sites by applying a quadratically constrained quadratic 0-1 solution to data (e.g. STLmax profiles) derived from pre-seizure onset and post-seizure onset EEG signals, monitoring a T-index curve of the identified critical sites, warning of an impending seizure, observing a seizure and then repeating the cycle by identifying critical sites using the new data, and then monitoring them.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Chaoticity profiles are then generated for each spatio-temporal response. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the chaoticity profiles associated with one or more critical channel groups of a selected predictor, where a predictor represents a given number of critical channel groups “x”, a given number of channels per group “y”, and a total number of channels N. Characterizing the behavior of the system is based on this determination.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Multiple dynamical profiles are then generated for each spatio-temporal response, where each of the multiple dynamical profiles correspond to a different one of multiple dynamical parameters. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the dynamical profiles associated with a selected one or a selected combination of dynamical parameters. Seizure warnings and/or predictions are provided based on this determination.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Multiple dynamical profiles are then generated for each spatio-temporal response, where each of the multiple dynamical profiles correspond to a different one of multiple dynamical parameters. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the dynamical profiles associated with a selected one or a selected combination of dynamical parameters. Seizure warnings and/or predictions are provided based on this determination.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Chaoticity profiles are then generated for each spatio-temporal response. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the chaoticity profiles associated with one or more critical channel groups of a selected predictor, where a predictor represents a given number of critical channel groups “x”, a given number of channels per group “y”, and a total number of channels N. Characterizing the behavior of the system is based on this determination.

Abstract: Impending seizure warning (ISW), seizure susceptibility period detection (SSPD), hours or days before an impending seizure, and time to impending seizure prediction (TISP) are provided by measuring each of a number of signals from different locations about a patient's brain, and generating therefrom, a spatio-temporal response based on these signals. Chaoticity profiles are then generated for each spatio-temporal response. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment exists between the chaoticity profiles associated with the responses from a set of critical locations. If so, a seizure warning, an indication of seizure susceptibility and a prediction of a time of occurrence of an impending seizure may be issued.