# US 2019/0167177 A1 · System and method for detecting acute brain function impairment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0167177 A1 - Document ID: US20190167177A1 - Status: Published application - Kind: A1 - Application number: 16/323,043 - Filed: 2017-08-04 - Published: 2019-06-06 - Earliest claimed: 2016-08-04 - Assignees: The General Hospital Corporation - Applicants: The General Hospital Corporation - Inventors: A. Eden Evins; Jodi M. Gilman - Web page: https://patentgrep.com/patent/US20190167177A1 ## CPC - A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)} - A61B 5/0075: {by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy (A61B5/0071 takes precedence)} - A61B 5/18: for vehicle drivers {or machine operators} - A61B 5/372: Analysis of electroencephalograms - A61B 5/7267: {involving training the classification device} - A61B 5/6803: {Head-worn items, e.g. helmets, masks, headphones or goggles} - G06N 20/00: Machine learning - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A system and method is provided for acquiring data from a brain of a subject and analyzing the data to generate an indication of a level of acute impairment of the subject. ## Claims ### Claim 1 A system for determining an acute brain function impairment of a subject, the system comprising: a sensor system configured to acquire signals indicative of a function of a brain of the subject; a processor configured to receive the signals from the sensor and configured to: analyze the signals to determine an amount of functional connectivity between portions of the brain of the subject from the signals; compare the amount of functional connectivity between portions of the brain of the subject to a reference amount of functional connectivity; determine an acute impairment of the subject based on the comparison of the amount of functional connectivity between portions of the brain of the subject to the reference amount of functional connectivity; and generate a report indicating the acute impairment of the subject. ### Claim 2 (depends on 1) The system of claim 1, wherein the reference amount of functional connectivity is acquired from the subject during a time of known non-impairment of the subject. ### Claim 3 (depends on 1) The system of claim 1, wherein the sensor system is configured to acquire the signals from the prefrontal cortex. ### Claim 4 (depends on 3) The system of claim 3, wherein the sensor system is configured to acquire the signals from an area of the brain outside the prefrontal cortex. ### Claim 5 (depends on 4) The system of claim 4, wherein the reference amount of functional connectivity is determined from the signals acquired from the area of the brain outside the prefrontal cortex. ### Claim 6 (depends on 1) The system of claim 1, wherein the sensor system includes a plurality of near infrared spectroscopy (NIRS) sensors configured to acquire the signals. ### Claim 7 (depends on 6) The system of claim 6, wherein the plurality of NIRS sensors includes eight sensors configured to be arranged along a forehead of the subject. ### Claim 8 (depends on 1) The system of claim 1, further comprising a headband configured to arrange the sensor system to acquire the signals from a prefrontal cortex of the subject. ### Claim 9 (depends on 1) The system of claim 1, further comprising a display configured to display the report. ### Claim 10 (depends on 1) The system of claim 1, wherein the processor is configured to implement a machine learning architecture to perform at least one of the analyzing, comparing, and determining. ### Claim 11 (depends on 10) The system of claim 10, wherein the machine learning architecture includes a random forest architecture trained on impairment data from subjects having been administered Δ9-tetrahydrocannabinol (THC). ### Claim 12 (depends on 1) The system of claim 1, wherein the report includes a quantitative indication of acute impairment of the individual based on an increase in the amount of connectivity between portions of the brain of the subject compared to the reference amount of connectivity. ### Claim 13 A method for detecting acute impairment, the method including steps comprising: a) acquiring, with a sensor, signals from a subject indicative of brain functionality of the subject; b) processing, using a computer processor, the signals to determine functional connectivity of the brain of the subject; c) determining, using the computer processor, whether the functional connectivity of the brain of the subject is indicative of an impairment of the subject; and d) generating a report indicating a level of acute impairment of the subject from the determining of step c). ### Claim 14 (depends on 13) The method of claim 13, wherein step b) includes determining a reference amount of functional connectivity and step c) includes determine an acute impairment of the subject based on the comparison of the amount of functional connectivity between portions of the brain of the subject to the reference amount of functional connectivity. ### Claim 15 (depends on 13) The method of claim 13, further comprising acquiring the reference amount of functional connectivity following a delay period after step a) selected to acquire data from the subject during a period of known non-impairment. ### Claim 16 (depends on 13) The method of claim 13, wherein step c) includes utilizing a machine learning model trained to analyze metabolizing of Δ9-tetrahydrocannabinol (THC) by the subject. ### Claim 17 (depends on 16) The method of claim 16, wherein the machine learning model includes a Support Vector Machine regression (SVM-R) model. ### Claim 18 (depends on 16) The method of claim 16, wherein the machine learning model includes a random forest model. ### Claim 19 (depends on 13) The method of claim 13, wherein step b) includes determining an oxy-hemoglobin (HbO) concentration in the subject from the signals and determining functional connectivity based on correlations between HbO concentration over time in the subject's brain. ### Claim 20 A portable system for detecting acute impairment of a subject, the system comprising: a detection system comprising: at least one sensor configured for placement on a head of the subject to acquire data indicating functionality of a brain of the subject; a processor configured to receive the data from the at least one sensor; a display screen in communication with the processor and configured to display at least an indication of an acute impairment state of the subject; and wherein the processor is configured to analyze the data and output an indication of the acute impairment state of the subject to the display. ### Claim 21 (depends on 20) The system of claim 20, wherein the at least one sensor is a functional Near-Infrared spectroscopy (fNIRS) sensor. ### Claim 22 (depends on 20) The system of claim 20, wherein the processor is configured to determine a metabolizing of Δ9-tetrahydrocannabinol (THC) in the subject from the data. ### Claim 23 (depends on 20) The system of claim 20, further comprising a substrate configured to couple the at least one sensor against the head of the subject to acquire the data from at least a prefrontal cortex of the subject. ### Claim 23 (depends on 20) The system of claim 20, wherein the processor is further configured to determine a activation and functional connectivity of the brain of the subject from the data and analyze a level of activation and functional connectivity of the brain of the subject to determine the indication of acute impairment state of the subject.