# US 11,998,342 B2 · Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,998,342 B2 - Document ID: US11998342B2 - Status: Granted - Kind: B2 - Application number: 18/054,363 - Filed: 2022-11-10 - Published: 2023-06-08 - Granted: 2024-06-04 - Earliest claimed: 2020-02-12 - Assignees: iRhythm Technologies, Inc. - Applicants: iRhythm Technologies, Inc. - Inventors: Steven Szabados; Yuriko Tamura; Xixi Wang; George Mathew - Web page: https://patentgrep.com/patent/US11998342B2 ## CPC - A61B 5/02405: {Determining heart rate variability} - A61B 5/361: Detecting fibrillation - A61B 5/0006: {ECG or EEG signals} - 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 5/257: using adhesive means, e.g. adhesive pads or tapes - A61B 5/28: for electrocardiography [ECG] - A61B 5/352: Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval - A61B 5/363: Detecting tachycardia or bradycardia - A61B 5/4809: {Sleep detection, i.e. determining whether a subject is asleep or not} - A61B 5/6801: {specially adapted to be attached to or worn on the body surface} - A61B 5/6823: {Trunk, e.g., chest, back, abdomen, hip} - A61B 5/6833: {Adhesive patches} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - 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} - G06F 21/6245: {Protecting personal data, e.g. for financial or medical purposes} - A61B 2560/0209: adapted for power saving - A61B 2560/0406: specially shaped apparatus housings - A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches - A61B 2562/164: the sensor is mounted in or on a conformable substrate or carrier - A61B 2562/166: the sensor is mounted on a specially adapted printed circuit board - A61B 2562/168: Fluid filled sensor housings - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Some embodiments include processing data via an executable file on a monitor to reduce the dimensionality of the data being transmitted over the wireless network. The output of the executable file also encrypts the data before being transmitted wireless to a remote server. The remote server receives the transmitted data and makes likelihood inferences based on the recorded data. ## Claims ### Claim 1 A monitor comprising: a first sensor positioned to detect a first set of continuous physiological signals of a user while the monitor is engaged to the user; a hardware processor configured to process the first set of continuous physiological signals to extract one or more features of the user; and a second sensor positioned to detect a second set of continuous physiological signals of the user in response to the one or more features meeting a threshold value; wherein the hardware processor is configured to process the second set of continuous physiological signals through an encoder to generate data output; and wherein an external computing system is configured to determine or infer a physiological event of the user by processing the data output or a signal derived from the data output through a decoder or a machine learning model. ### Claim 2 (depends on 1) The monitor of claim 1, wherein the physiological event comprises at least one of: hypertension, depression, congestive heart failure, sleep apnea. ### Claim 3 (depends on 1) The monitor of claim 1, wherein the monitor is a wearable patch. ### Claim 4 (depends on 1) The monitor of claim 1, wherein the monitor is a patch that is applied on the chest of the user. ### Claim 5 (depends on 1) The monitor of claim 1, wherein the monitor is a wearable watch. ### Claim 6 (depends on 1) The monitor of claim 1, wherein the data output of the encoder is an encrypted output, and wherein the external computing system is configured to processes the encrypted output through the decoder or the machine learning model. ### Claim 7 (depends on 1) The monitor of claim 1, wherein the first set of continuous physiological signals comprise Photoplethysmography (PPG) data. ### Claim 8 (depends on 1) The monitor of claim 1, wherein the second set of continuous physiological signals comprise electrocardiogram (ECG) data. ### Claim 9 (depends on 1) The monitor of claim 1, wherein the first set of continuous physiological signals comprise electrocardiogram (ECG) data and the second set of continuous physiological signals comprise Photoplethysmography (PPG) data. ### Claim 10 (depends on 1) The monitor of claim 1, wherein the encoder is configured to compress the second set of continuous physiological signals to obtain compressed data, and wherein the decoder or the machine learning model is configured to decompress the compressed data. ### Claim 11 A monitor comprising: a sensor configured to detect a first set of signals of a patient when engaged with a body of the patient; a signal processor configured to process the first set of signals to extract one or more derived signals; and a second sensor positioned to detect a second set of signals of the patient based on the one or more derived signals; wherein the signal processor is further configured to process the second set of signals through a portion of a first machine learning network to generate a first output, wherein the signal processor is local to the monitor; and wherein a computing system is configured to process the first output or a signal derived from the first output through a portion of a second machine learning network, wherein the computing system is external to the monitor. ### Claim 12 (depends on 11) The monitor of claim 11, wherein the portion of the first machine learning network comprises a subset of the first machine learning network. ### Claim 13 (depends on 11) The monitor of claim 11, wherein the portion of the first machine learning network comprises the first machine learning network. ### Claim 14 (depends on 11) The monitor of claim 11, wherein the monitor includes a transmitter configured to transmit the first output to the computing system. ### Claim 15 (depends on 11) The monitor of claim 11, wherein a dimensionality of the first output is smaller than a dimensionality of the second set of signals detected by the second sensor. ### Claim 16 A monitor comprising: a sensor positioned to detect a first set of physiological signals of a user while the monitor is engaged to the user; a hardware processor configured to process the first set of physiological signals to extract one or more features of the user; and a second sensor positioned to detect a second set of physiological signals of the user in response to the one or more features meeting a condition; wherein an external computing system is configured to determine or infer a physiological characteristic of the user by processing the second set of physiological signals or a signal derived from the second set of physiological signals through a subset of layers of a neural network. ### Claim 17 (depends on 16) The monitor of claim 16, wherein the physiological characteristic comprises a likelihood of an occurrence of arrhythmia during a time period prior to when the first set of physiological signals were detected. ### Claim 18 (depends on 16) The monitor of claim 16, wherein the physiological characteristic comprises a likelihood of an occurrence of arrhythmia during a time period subsequent to when the first set of physiological signals were detected. ### Claim 19 (depends on 16) The monitor of claim 16, wherein the physiological characteristic comprises at least one of: a heart abnormality, a heart failure, a prognostic prediction, sleep apnea, hypertension, or blood pressure. ### Claim 20 (depends on 16) The monitor of claim 16, wherein the condition comprises one or more features meeting a threshold or elapsing of a time period.