Detecting, assessing and managing epilepsy using a multi-variate, metric-based classification analysis
US-2017079573-A1 · Mar 23, 2017 · US
US11998360B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11998360-B2 |
| Application number | US-202117352499-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jun 21, 2021 |
| Priority date | Feb 17, 2015 |
| Publication date | Jun 4, 2024 |
| Grant date | Jun 4, 2024 |
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A wearable device attached to a subject includes an accelerometer that measures acceleration information, and a biological sensor that measures biological signal information of the subject. From the measured acceleration information and biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period are extracted. By machine learning based on the first feature data, a dynamic/static activity identification model, a dynamic-activity identification model, and a static-activity identification model, for the subject, are generated. By combination of results of determination based on each of the identification models, a posture and an activity of the subject are identified and correspondence information, which associates the identified posture and activity with the biological signal information of the subject, is generated.
Opening claim text (preview).
The invention claimed is: 1. An autonomic function information acquisition device, comprising: a wearable device that has a shirt shape and is configured to be attached to a trunk of a subject; an acceleration sensor that is provided in the wearable device, and measures acceleration information of motion of the subject whom the wearable device is attached to; a biological signal information sensing device that is provided in the wearable device, and measures biological signal information of the subject; and processing circuitry configured to identify, by executing sequential machine learning for the acceleration information and the biological signal information in a first predetermined period, a posture and an activity of the subject in a second predetermined period; extract biological signal information corresponding to a combination of the same posture and activity identified; and calculate a parameter of autonomic function evaluation from the biological signal information extracted and corresponding to the combination of the same posture and activity. 2. The autonomic function information acquisition device according to claim 1 , wherein the biological signal information sensing device measures, as the biological signal information, heart rate data of the subject, and the processing circuitry extracts heart rate data corresponding to the combination of the same posture and activity, and calculates, as the parameter, at least one of an average value, a variance value, and a median point, of the heart rate data extracted by the processing circuitry. 3. The autonomic function information acquisition device according to claim 2 , wherein the processing circuitry connects together the extracted heart rate data corresponding to the combination of the same posture and activity, into a group of data, wherein when a difference between values of connected portions of the connected heart rate data is less than a predetermined threshold, the processing circuitry statistically calculates an estimated value that corrects the difference, and connects together the heart rate data that have been corrected by the estimated value. 4. The autonomic function information acquisition device according to claim 2 , wherein the processing circuitry connects together the extracted heart rate data corresponding to the combination of the same posture and activity, into a group of data, wherein when a difference between values of connected portions of the connected heart rate data is equal to or larger than a predetermined threshold, the processing circuitry deletes a value exceeding the predetermined threshold and connects the heart rate data together. 5. The autonomic function information acquisition device according to claim 1 , wherein the biological signal information sensing device measures, as the biological signal information, heart rate data of the subject, and the processing circuitry extracts plural sets of heart rate data corresponding to periods corresponding to the same consecutive changes in posture and activity, and synchronously adds together the plural sets of heart rate data by synchronizing starting time points or ending time points of a change in the posture or activity in the plural sets of heart rate data extracted by the processing circuitry, and calculates the parameter from the synchronously added data. 6. The autonomic function information acquisition device according to claim 5 , wherein the processing circuitry extracts the plural sets of heart rate data corresponding to the periods corresponding to the same consecutive changes in posture and activity, each of the periods being a period, in which the subject changes a posture, and in which a body of the subject is static before and after a time point of the change of the posture, and calculates, as the parameter, at least one of a difference between average heartbeat intervals before and after the time point of the change of the posture; a maximum inclination of heartbeat intervals in an initial response; and a maximum inclination of heartbeat intervals in a late response. 7. The autonomic function information acquisition device according to claim 5 , wherein the processing circuitry extracts the plural sets of heart rate data corresponding to the periods corresponding to the same consecutive changes in posture and activity, each of the periods being a period, in which the subject changes from a static state to a dynamic state and thereafter returns to the static state again, and during which a posture of the subject does not change, and synchronously adds together the plural sets of heart rate data by synchronizing starting time points of the dynamic state, and calculates, as the parameter, at least one of: a maximum inclination of a rising phase of heart rate; an average heart rate after the rise; and a difference between average heart rates or average heartbeat intervals before start of the dynamic state and during the dynamic state. 8. The autonomic function information acquisition device according to claim 5 , wherein the processing circuitry extracts plural sets of heart rate data corresponding to the periods corresponding to the same consecutive changes in posture and activity, each of the periods being a period, in which the subject changes from a static state to a dynamic state and thereafter returns to a static state again, and during which the posture of the subject does not change, and synchronously adds together the plural sets of heart rate data by synchronizing ending time points of the dynamic state, and calculates, as the parameter, at least one of: a maximum inclination of a heart rate falling phase; an average heart rate after the fall; and a difference between average heart rates or average heartbeat intervals during the dynamic state and after ending of the dynamic state. 9. The autonomic function information acquisition device according to claim 1 , wherein the processing circuitry executes machine learning based on correspondence between the posture and activity identified by the processing circuitry and the biological signal information; and detects, based on a result of the machine learning, an abnormality in the subject from the acceleration information and the biological signal information measured by the acceleration sensor and the biological signal information sensing device. 10. The autonomic function information acquisition device according to claim 1 , wherein the first predetermined period and the second predetermined period overlap each other at least partially. 11. An autonomic function information acquisition method, comprising: a reception step of receiving respectively, from an acceleration sensor and a biological signal information sensing device that are provided in a wearable device that has a shirt shape and is configured to be attached to a trunk of a subject, acceleration information on motion of a subject whom the wearable device is attached to, and biological signal information of the subject; an identification step of identifying, by executing sequential machine learning for the acceleration information and the biological signal information in a first predetermined period, a posture and a motion of the subject in a second predetermined period; an extraction step of extracting biological signal information corresponding to a combination of the same posture and activity identified in the identification step; and a calculation step of calculating a parameter of autonomic function evaluation from the biological signal information extracted in the extraction step and corresponding to the combination of the same posture and activity. 12
using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured · CPC title
Measuring pulse rate or heart rate · CPC title
Determining heart rate variability · CPC title
Determining posture transitions · CPC title
Determining activity level · CPC title
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