System, process, and devices for real-time brain monitoring

A method of real-time brain monitoring for anxiety or epilepsy by processing real-time raw sensor data and continuously updating visual representations of a real-time brain state of a patient and corresponding treatment guidance, the method comprising:

• receiving sensor data obtained by a collector device coupled to a plurality of sensors for pre-processing real-time raw sensor data acquired from the plurality of sensors including a subset of sensors for monitoring a patient's brain, each sensor of the subset of sensors corresponding to a channel and a location relative to the patient's brain;
• determining, using the sensor data, a connectivity matrix having connectivity values, a connectivity value for each pair of channels, and a real-time brain value index corresponding to a real-time brain state of the patient, the real-time brain state of the patient comprising a real-time anxiety state of the patient or a real-time epileptic spasm state of the patient, the brain value index determined based on a total number of possible pairs of channels given a specific channel montage N=Nc!/2!(Nc−p)!, Nc being a number of channels, p being a number of connected pairs of channels, p being calculated using a threshold value and the connectivity values of the connectivity matrix, wherein the processor implements machine learning to compute the brain value index based on historical data for the patient or other patients;
• determining treatment data using the brain value index corresponding to the real-time brain state of the patient, wherein the treatment data provides one or more of a monitoring state to continue a current treatment for the patient, an intervention state to re-evaluate the patient and adjust the current treatment for the patient, and a resuscitate state to urgently intervene in the current treatment for the patient, wherein the treatment data is specific for a level of anxiety within a plurality of levels of anxiety upon determining that the real-time brain state of the patient comprises the real-time anxiety state of the patient, wherein each of the plurality of levels of anxiety is associated with a threshold value or range of values configured for the patient, wherein the brain value index can be compared with the threshold value or range of values to determine the level of anxiety of the patient, wherein the treatment data is specific for an epileptic event of the patient of a plurality of epileptic events upon determining that the real-time brain state of the patient comprises the real-time epileptic spasm state of the patient, wherein each of the plurality of epileptic events is associated with a threshold value or range of values configured for the patient, wherein the brain value index can be compared with the threshold value or range of values to determine the epileptic event of the patient;
• generating treatment guidance using the brain value index and the treatment data, wherein the processor implements machine learning to generate recommended treatments as part of the treatment guidance based on historical data for the patient or other patients, wherein the treatment guidance corresponds to the level of anxiety of the patient upon determining that the real-time brain state of the patient comprises the real-time anxiety state of the patient, wherein the treatment guidance corresponds to a number of epileptic spasms and hypsarrhythmia patterns of the patient upon determining that the real-time brain state of the patient comprises the real-time epileptic spasm state of the patient;
• generating and updating visual elements for an interface in real-time, the visual elements representing the real-time brain value index to depict the brain state of the patient, the treatment guidance with the recommended treatments, and a connectivity map for the connectivity matrix, the connectivity map visually indicating a head map for the patient with the arrangement of the channels of the subset of sensors and a connecting line between a pair of channels visually depicting a strength of connection between the pair of channels based on the respective connectivity value for the pair of channels, the at least one processor configured to issue control commands to continuously update the interface in real-time using the generated visual elements;
• sending signals to display the interface with the visual elements based on the issued control commands from the server to continuously update the interface in real-time with the visual elements;
• and
• automatically generating alerts based on the treatment data that provides one or more of the monitoring state, the intervention state and the resuscitate state, and transmitting alert notifications in response to detecting specific real-time brain states.

A system for real-time brain monitoring for anxiety or epilepsy by processing real-time raw sensor data and continuously updating visual representations of a real-time brain state of a patient and corresponding treatment guidance, the system comprising:

• at least one processor;
• and
• a memory comprising instructions which, when executed by the at least one processor, configure the at least one processor to:
• receive sensor data obtained by a collector device coupled to a plurality of sensors for pre-processing real-time raw sensor data acquired from the plurality of sensors including a subset of sensors for monitoring a patient's brain, each sensor of the subset of sensors corresponding to a channel and a location relative to the patient's brain;
• determine a connectivity matrix having connectivity values, a connectivity value for each pair of channels, a real-time brain value index and treatment data using the sensor data, the real-time brain value index corresponding to a real-time brain state of the patient, the real-time brain state of the patient comprising a real-time anxiety state of the patient or a real-time epileptic spasm state of the patient, the brain value index determined based on a total number of possible pairs of channels given a specific channel montage N=Nc!/2!(Nc−p)!, Nc being a number of channels, p being a number of connected pairs of channels, p being calculated using a threshold value and the connectivity values of the connectivity matrix, wherein the processor implements machine learning to compute the brain value index based on historical data for the patient or other patients;
• determine treatment data using the brain value index corresponding to the real-time brain state of the patient, wherein the treatment data provides one or more of a monitoring state to continue a current treatment for the patient, an intervention state to re-evaluate the patient and adjust the current treatment for the patient, and a resuscitate state to urgently intervene in the current treatment for the patient, wherein the treatment data is specific for a level of anxiety of a plurality of levels of anxiety upon determining that the real-time brain state of the patient comprises the real-time anxiety state of the patient, wherein each of the plurality of levels of anxiety is associated with a threshold value or range of values configured for the patient, wherein the brain value index can be compared with the threshold value or range of values to determine the level of anxiety of the patient, wherein the treatment data is specific for an epileptic event of the patient of a plurality of epileptic events upon determining that the real-time brain state of the patient comprises the real-time epileptic spasm state of the patient, wherein each of the plurality of epileptic events is associated with a threshold value or range of values configured for the patient, wherein the brain value index can be compared with the threshold value or range of values to determine the epileptic event of the patient;
• generate treatment guidance using the brain value index and the treatment data, wherein the processor implements machine learning to generate recommended treatments as part of the treatment guidance based on historical data for the patient or other patients, wherein the treatment guidance corresponds to the level of anxiety of the patient upon determining that the real-time brain state of the patient comprises the real-time anxiety state of the patient, wherein the treatment guidance corresponds to a number of epileptic spasms and hypsarrhythmia patterns of the patient upon determining that the real-time brain state of the patient comprises the real-time epileptic spasm state of the patient;
• generate and update visual elements for an interface in real-time, the visual elements representing a connectivity map for the connectivity matrix, the treatment guidance with the recommended treatments, the real-time brain value index and the treatment data, the visual elements depict the channels, connections between the channels, and strengths of the connections, the at least one processor configured to issue control commands to continuously update the interface in real-time;
• send signals to display the interface the interface with the visual elements based on the issued control commands from the server to continuously update the interface in real-time with the visual elements;
• and
• automatically generate alerts based on the treatment data that provides one or more of the monitoring state, the intervention state and the resuscitate state, and transmit alert notifications in response to detecting specific real-time brain states.

A method of real-time brain monitoring for anxiety or epilepsy by processing real-time raw sensor data and continuously updating visual representations of a real-time brain state of a patient and corresponding treatment guidance, the method comprising:

• receiving sensor data obtained by a collector device coupled to a plurality of sensors for pre-processing real-time raw sensor data acquired from the plurality of sensors including a subset of sensors for monitoring a patient's brain, each sensor of the subset of sensors corresponding to a channel and a location relative to the patient's brain;
• determining a connectivity matrix having connectivity values, a connectivity value for each pair of channels, a real-time brain value index and treatment data using the sensor data, the real-time brain value index corresponding to a real-time brain state of the patient, the real-time brain state of the patient comprising a real-time anxiety state of the patient or a real-time epileptic spasm state of the patient, the brain value index determined based on a total number of possible pairs of channels given a specific channel montage N=Nc!/2!(Nc−p)!, Nc being a number of channels, p being a number of connected pairs of channels, p being calculated using a threshold value and the connectivity values of the connectivity matrix, wherein the processor implements machine learning to compute the brain value index based on historical data for the patient or other patients;
• determining treatment data using the brain value index corresponding to the real-time brain state of the patient, wherein the treatment data provides one or more of a monitoring state to continue a current treatment for the patient, an intervention state to re-evaluate the patient and adjust the current treatment for the patient, and a resuscitate state to urgently intervene in the current treatment for the patient, wherein the treatment data is specific for a level of anxiety of a plurality of levels of anxiety upon determining that the real-time brain state of the patient comprises the real-time anxiety state of the patient, wherein each of the plurality of levels of anxiety is associated with a threshold value or range of values configured for the patient, wherein the brain value index can be compared with the threshold value or range of values to determine the level of anxiety of the patient, wherein the treatment data is specific for an epileptic event of the patient of a plurality of epileptic events upon determining that the real-time brain state of the patient comprises the real-time epileptic spasm state of the patient, wherein each of the plurality of epileptic events is associated with a threshold value or range of values configured for the patient, wherein the brain value index can be compared with the threshold value or range of values to determine the epileptic event of the patient;
• generating treatment guidance using the brain value index and the treatment data, wherein the processor implements machine learning to generate recommended treatments as part of the treatment guidance based on historical data for the patient or other patients, wherein the treatment guidance corresponds to the level of anxiety of the patient upon determining that the real-time brain state of the patient comprises the real-time anxiety state of the patient, wherein the treatment guidance corresponds to a number of epileptic spasms and hypsarrhythmia patterns of the patient upon determining that the real-time brain state of the patient comprises the real-time epileptic spasm state of the patient;
• generating and updating visual elements for an interface in real-time, the visual elements representing a connectivity map for the connectivity matrix, the treatment guidance with the recommended treatments, the real-time brain value index and the treatment data, the visual elements depict the channels, connections between the channels, and strengths of the connections, the at least one processor configured to issue control commands to continuously update the interface in real-time;
• sending signals to display the interface the interface with the visual elements based on the issued control commands from the server to continuously update the interface in real-time with the visual elements;
• and
• automatically generating alerts based on the treatment data that provides one or more of the monitoring state, the intervention state and the resuscitate state, and transmitting alert notifications in response to detecting specific real-time brain states.