# US 10,770,182 B2 · Systems and methods for assessing the health status of a patient > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,770,182 B2 - Document ID: US10770182B2 - Status: Granted - Kind: B2 - Application number: 15/982,506 - Filed: 2018-05-17 - Published: 2018-11-22 - Granted: 2020-09-08 - Earliest claimed: 2017-05-19 - Assignees: Boston Scientific Scimed, Inc. - Applicants: Cardiac Pacemakers, Inc. - Inventors: Gregory J. Sherwood; Kyle Harish Srivastava; Bryan Allen Clark; Justin Theodore Nelson; Carl Walter Bauer - Web page: https://patentgrep.com/patent/US10770182B2 ## CPC - G16H 50/30: for calculating health indices; for individual health risk assessment - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - A61B 5/021: Measuring pressure in heart or blood vessels - A61B 5/082: {Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath (A61B5/083, A61B5/091 take precedence)} - A61B 5/681: {Wristwatch-type devices} - A61B 5/686: {Permanently implanted devices, e.g. pacemakers, other stimulators, biochips (A61B5/6861 takes precedence)} - G01N 33/4975: {other than oxygen, carbon dioxide or alcohol, e.g. organic vapours} - G16H 10/65: stored on portable record carriers, e.g. on smartcards, RFID tags or CD - A61B 5/02405: {Determining heart rate variability} - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) ## Abstract Embodiments herein include medical systems, devices, and methods for assessing the health status of a patient. In an embodiment, a method includes evaluating the presence of volatile organic compounds in a breath or gas sample of the patient with a plurality of graphene sensors to generate volatile organic compound data, wherein the plurality of graphene sensors include sensors that are specific for different volatile organic compounds. The method can further include collecting data regarding the patient's sympathetic nervous activity. The method can further include combining the volatile organic compound data with the collected data regarding the patient's sympathetic nervous activity to form a combined data set. The method can further include matching the combined data set against one or more data patterns to find the best match, the best match indicating the health status of the patient. Other embodiments are also included herein. ## Claims ### Claim 1 A method of assessing the health status of a patient comprising: evaluating the presence of volatile organic compounds in a breath or gas sample of the patient with a plurality of graphene sensors to generate volatile organic compound data, wherein the plurality of graphene sensors include sensors that are specific for different volatile organic compounds; collecting data regarding the patient's sympathetic nervous activity, the data comprising at least one of heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); combining the volatile organic compound data with the collected data regarding the patient's sympathetic nervous activity to form a combined data set; and matching the combined data set against one or more previously determined data patterns using a pattern matching algorithm to determine the data pattern that is the best match, wherein the specific previously determined data pattern that is the best match indicates the health status of the patient. ### Claim 2 (depends on 1) The method of claim 1, wherein the one or more previously determined data patterns are created using a machine learning process. ### Claim 3 (depends on 1) The method of claim 1, further comprising collecting data regarding the patient's functional status, the data comprising gait data; and adding the data regarding the patient's functional status to the combined data set. ### Claim 4 (depends on 1) The method of claim 1, further comprising collecting data regarding the patient's demographic features; and adding the data regarding the patient's demographic features to the combined data set. ### Claim 5 (depends on 1) The method of claim 1, wherein collecting the data regarding the patient's sympathetic nervous activity is performed in a non-clinical setting and evaluating the presence of the volatile organic compounds is performed in a clinical setting. ### Claim 6 (depends on 1) The method of claim 1, wherein collecting the data regarding the patient's sympathetic nervous activity is performed with a wearable device. ### Claim 7 (depends on 1) The method of claim 1, wherein collecting the data regarding the patient's sympathetic nervous activity is performed over a time period of at least about 1 day. ### Claim 8 (depends on 1) The method of claim 1, wherein collecting the data regarding the patient's sympathetic nervous activity is performed with an implanted device. ### Claim 9 (depends on 1) The method of claim 1, wherein the volatile organic compound data from the breath or gas sample of the patient is downloaded from an external breath sensing system onto at least one of a wearable device and an implantable device. ### Claim 10 (depends on 1) The method of claim 1, wherein the collected data regarding the patient's sympathetic nervous activity is uploaded from a wearable device to a clinical diagnostic device. ### Claim 11 (depends on 1) The method of claim 1, wherein one or more of the plurality of graphene sensors are chosen as controls on the collected data regarding the patient's sympathetic nervous activity. ### Claim 12 (depends on 11) The method of claim 11, wherein the controls correlate with sympathetic nervous activity. ### Claim 13 (depends on 12) The method of claim 12, further comprising generating a notification if the measured values of the controls do not match the measured values of sympathetic nervous activity. ### Claim 14 (depends on 1) The method of claim 1, wherein the collected data regarding the patient's sympathetic nervous activity reflects a baseline level of sympathetic nervous activity and changes over the baseline level of sympathetic nervous activity. ### Claim 15 (depends on 1) The method of claim 1, wherein the plurality of graphene sensors can detect the presence of at least 10 different volatile organic compounds. ### Claim 16 A diagnostic health system comprising: a communications circuit; a memory circuit; and a processor in electronic communication with the communication circuit and the memory circuit, the processor is configured to combine volatile organic compound data with collected data regarding a patient's sympathetic nervous activity to form a combined data set, the collected data comprising at least one of heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); and match the combined data set against one or more previously determined data patterns using a pattern matching algorithm to determine a pattern that is the best match, wherein the specific previously determined pattern that is the best match indicates the health status of the patient; and report the health status of the patient based on the best pattern match. ### Claim 17 (depends on 16) The diagnostic health system of claim 16, wherein the diagnostic health system is a wearable device and the volatile organic compound data is downloaded onto the wearable device from another device. ### Claim 18 (depends on 16) The diagnostic health system of claim 16, wherein the diagnostic health system is disposed in a clinical environment and collected data regarding a patient's sympathetic nervous activity is uploaded to the diagnostic health system from a wearable device. ### Claim 19 A diagnostic health system comprising: a patient-specific device selected from the group consisting of a wearable device and an implanted device; and an external breath sensing system; and a processor receiving data from the patient-specific device and the external breath sensing system; wherein the patient-specific device collects data regarding a patient's sympathetic nervous activity, the data comprising at least one of heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); wherein the external breath sensing system collects data regarding the presence of volatile organic compounds in a breath or gas sample of the patient; and wherein the processor is configured to combine the volatile organic compound data with the patient's sympathetic nervous activity data to form a combined data set; and match the combined data set against one or more previously determined data patterns using a pattern matching algorithm to determine a pattern that is the best match, wherein the specific previously determined pattern that is the best match indicates the health status of the patient; and report the health status of the patient based on the best pattern match.