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1. WO2022011106 - HEART RATE CORRECTION USING EXTERNAL DATA

Publication Number WO/2022/011106
Publication Date 13.01.2022
International Application No. PCT/US2021/040830
International Filing Date 08.07.2021
IPC
A61B 5/024 2006.1
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
02Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
024Measuring pulse rate or heart rate
A61B 5/00 2006.1
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
G16H 50/20 2018.1
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 2018.1
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
50for simulation or modelling of medical disorders
CPC
A61B 2562/0219
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
2562Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
02Details of sensors specially adapted for in-vivo measurements
0219Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
A61B 5/02427
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
024Detecting, measuring or recording pulse rate or heart rate
02416using photoplethysmograph signals, e.g. generated by infra-red radiation
02427Details of sensor
A61B 5/721
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
72Signal processing specially adapted for physiological signals or for diagnostic purposes
7203for noise prevention, reduction or removal
7207of noise induced by motion artifacts
721using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
A61B 5/7257
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
72Signal processing specially adapted for physiological signals or for diagnostic purposes
7235Details of waveform analysis
7253characterised by using transforms
7257using Fourier transforms
A61B 5/7264
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
72Signal processing specially adapted for physiological signals or for diagnostic purposes
7235Details of waveform analysis
7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
A61B 5/7267
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes
72Signal processing specially adapted for physiological signals or for diagnostic purposes
7235Details of waveform analysis
7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
7267involving training the classification device
Applicants
  • OWLET BABY CARE INC. [US]/[US]
Inventors
  • JONES, Cory
  • CHRISTENSEN, Tanner
Agents
  • HANSEN, Jed, H.
  • DE JONGE, Peter, M.
  • PERRY, Steve, M.
  • HOBSON, Garron, M.
  • OAKESON, Gary, P.
Priority Data
63/049,58508.07.2020US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) HEART RATE CORRECTION USING EXTERNAL DATA
(FR) CORRECTION DE FRÉQUENCE CARDIAQUE AU MOYEN DE DONNÉES EXTERNES
Abstract
(EN) A technology for improving heart rate prediction. In one example, a machine learning model can be trained using photoplethysmogram (PPG) data to classify a signal in a dataset obtained from a generic data source as a heart rate. Thereafter, a PPG dataset generated by a PPG sensor can be input to the machine learning model to classify a PPG signal in the PPG dataset as a predicted heart rate. Accelerometer data generated by an accelerometer can be input to the machine learning model to classify an acceleration signal contained in the accelerometer data as a heart rate, where movement represented by the acceleration signal mimics the heart rate. The PPG signal in the PPG dataset can be identified as corresponding to the acceleration signal that mimics the heart rate, and the PPG signal can be removed to improve the accuracy of the heart rate prediction.
(FR) La présente invention concerne une technologie pour améliorer la prédiction de la fréquence cardiaque. Dans un exemple, un modèle d’apprentissage automatique peut être entraîné au moyen de données de photopléthysmogramme (PPG) pour classer un signal dans un ensemble de données obtenu à partir d’une source de données génériques comme étant une fréquence cardiaque. Ensuite, un ensemble de données PPG généré par un capteur PPG peut être entré dans le modèle d’apprentissage automatique pour classer un signal PPG dans l’ensemble de données PPG comme étant une fréquence cardiaque prédite. Des données d’accéléromètre générées par un accéléromètre peuvent être entrées dans le modèle d’apprentissage automatique pour classer un signal d’accélération contenu dans les données d’accéléromètre comme étant une fréquence cardiaque, le mouvement représenté par le signal d’accélération imitant la fréquence cardiaque. Le signal PPG dans l’ensemble de données PPG peut être identifié comme correspondant au signal d’accélération qui imite la fréquence cardiaque, et le signal PPG peut être éliminé pour améliorer l’exactitude de la prédiction de fréquence cardiaque.
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