# US 2025/0375147 A1 · Seizure detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0375147 A1 - Document ID: US20250375147A1 - Status: Published application - Kind: A1 - Application number: 18/812,491 - Filed: 2024-08-22 - Published: 2025-12-11 - Earliest claimed: 2024-06-11 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., Ltd. - Inventors: Quan Dong; Chaoyi Kang; Yu Hao Chang; Matthew Wiggins - Web page: https://patentgrep.com/patent/US20250375147A1 ## CPC - A61B 5/4094: {Diagnosing or monitoring seizure diseases, e.g. epilepsy} - A61B 5/681: {Wristwatch-type devices} - 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 5/0533: Measuring galvanic skin response - A61B 5/1117: {Fall detection} - A61B 5/6803: {Head-worn items, e.g. helmets, masks, headphones or goggles} - A61B 5/7221: {Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217)} - A61B 5/7267: {involving training the classification device} - A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms} - A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)} - A61B 5/031: {Intracranial pressure} - A61B 5/11: Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)} - A61B 5/14551: {for measuring blood gases} - A61B 5/296: for electromyography [EMG] - A61B 2560/0462: Apparatus with built-in sensors - A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract In one embodiment, a method includes detecting, by each of multiple sensors of a wearable device worn by a user, a physiological signal of the user; determining, by the wearable device, whether at least one of the detected physiological signals indicates that the user is suffering a seizure; and in response to a determination that the user is suffering a seizure, then determining, by a trained neural network and based on multiple detected physiological signals, a seizure condition of the user. ## Claims ### Claim 1 A method comprising: detecting, by each of a plurality of sensors of a wearable device worn by a user, a physiological signal of the user; determining, by the wearable device, whether at least one of the detected physiological signals indicates that the user is suffering a seizure; and in response to a determination that the user is suffering a seizure, then determining, by a trained neural network and based on a plurality of the detected physiological signals, a seizure condition of the user. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of sensors comprises at least two of: an accelerometer, an EMG sensor, a skin-impedance sensor, an SpO2 sensor, and a PPG sensor. ### Claim 3 (depends on 1) The method of claim 1, wherein determining whether at least one of the detected physiological signals indicates that the user is suffering a seizure comprises determining that at least one of the detected physiological signals meets a predetermined threshold specific to that physiological signal. ### Claim 4 (depends on 3) The method of claim 3, wherein the predetermined threshold is defined at least in part by one or more user characteristics of the user. ### Claim 5 (depends on 1) The method of claim 1, further comprising: determining, by the wearable device, whether each of the detected physiological signals meets a corresponding first signal-quality threshold; and in response to a determination that a detected physiological signal does not meet a corresponding first signal-quality threshold, then discarding that physiological signal prior to determining whether at least one of the detected physiological signals indicates that the user is suffering a seizure. ### Claim 6 (depends on 5) The method of claim 5, further comprising: determining, by the wearable device, whether each of the detected physiological signals meets a corresponding second signal-quality threshold; and in response to a determination that a detected physiological signal does not meet a corresponding second signal-quality threshold, then discarding that physiological signal prior to determining a seizure condition of the user. ### Claim 7 (depends on 1) The method of claim 1, wherein determining a seizure condition of the user comprises determining, by the neural network, whether the user suffered a seizure. ### Claim 8 (depends on 7) The method of claim 7, wherein determining a seizure condition of the user further comprises determining one or more of (1) an onset of the seizure and (2) a duration of the seizure. ### Claim 9 (depends on 1) The method of claim 1, wherein determining a seizure condition of the user comprises classifying, by the neural network, a type of seizure suffered by the user. ### Claim 10 (depends on 9) The method of claim 9, further comprising determining, for each of a plurality of types of seizures, a probability that the user suffered that particular type of seizure. ### Claim 11 (depends on 10) The method of claim 10, wherein the probability is determined at least in part on the user's seizure history. ### Claim 12 (depends on 1) The method of claim 1, wherein the trained neural network is deployed on the wearable device. ### Claim 13 (depends on 1) The method of claim 1, further comprising determining, by a trained discriminator and based on the determined seizure condition, a priority of the seizure condition. ### Claim 14 (depends on 13) The method of claim 13, wherein the priority of the seizure condition is further determined based on the user's seizure history. ### Claim 15 (depends on 14) The method of claim 14, wherein the user's seizure history comprises input from a health-care provider. ### Claim 16 (depends on 15) The method of claim 15, further comprising finetuning the trained neural network based at least in part on the input from the health-care provider. ### Claim 17 (depends on 1) The method of claim 1, further comprising providing, to the user, a notification regarding the seizure condition. ### Claim 18 (depends on 1) The method of claim 1, further comprising: determining a severity of the seizure condition of the user; and making, based on the determined severity, a request for emergency aid for the user. ### Claim 19 A wearable device comprising: a plurality of sensors, each sensor configured to detect a physiological signal of a user wearing the wearable device; one or more non-transitory computer readable storage media storing instructions; and one or more processors coupled to the one or more non-transitory computer readable storage media and operable to execute the instructions to: access, from each of the plurality of sensors, a respective detected physiological signal of the user; determine whether at least one of the detected physiological signals indicates that the user is suffering a seizure; and in response to a determination that the user is suffering a seizure, then determine, by a trained neural network and based on a plurality of the detected physiological signals, a seizure condition of the user. ### Claim 20 An apparatus comprising: a wearable device comprising: a plurality of sensors, each sensor configured to detect a physiological signal of a user wearing the wearable device; one or more first non-transitory computer readable storage media storing instructions; and one or more first processors coupled to the one or more first non-transitory computer readable storage media and operable to execute the instructions to: access, from each of the plurality of sensors, a respective detected physiological signal of the user; determine whether at least one of the detected physiological signals indicates that the user is suffering a seizure; and in response to a determination that the user is suffering a seizure, then provide, to a computing device, one or more of the detected physiological signals; and the computing device, comprising one or more second non-transitory computer readable storage media storing instructions; and one or more second processors coupled to the one or more second non-transitory computer readable storage media and operable to execute the instructions to: access the one or more detected physiological signals provided by the wearable device; and determine, by a trained neural network on the computing device and based on a plurality of the accessed physiological signals, a seizure condition of the user.