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1. WO2020112739 - MULTI-CHANNEL ECG AND WITH RHYTHM TRANSFER LEARNING

Publication Number WO/2020/112739
Publication Date 04.06.2020
International Application No. PCT/US2019/063203
International Filing Date 26.11.2019
IPC
G16H 50/20 2018.01
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
G06K 9/00 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06N 3/04 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
CPC
G06K 9/00536
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00496Recognising patterns in signals and combinations thereof
00536Classification; Matching
G06K 9/6273
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6267Classification techniques
6268relating to the classification paradigm, e.g. parametric or non-parametric approaches
627based on distances between the pattern to be recognised and training or reference patterns
6271based on distances to prototypes
6272based on distances to cluster centroïds
6273Smoothing the distance, e.g. Radial Basis Function Networks
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] 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
Applicants
  • PREVENTICE TECHNOLOGIES, INC. [US]/[US]
Inventors
  • TEPLITZKY, Benjamin Adam
  • MCROBERTS, Michael Thomas Edward
  • MEHTA, Pooja Rajiv
Agents
  • MCCLELLAN, Gero G.
  • PATTERSON, B. Todd
Priority Data
62/773,81730.11.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) MULTI-CHANNEL ECG AND WITH RHYTHM TRANSFER LEARNING
(FR) APPRENTISSAGE À CANAUX MULTIPLES ET À TRANSFERT DE RYTHME
Abstract
(EN)
Techniques for classifying cardiac events in electrocardiogram (ECG) data. A feature set is generated by analyzing ECG data for a patient using a first phase in a machine learning architecture. A first cardiac event in the ECG data is classified based on the feature set, using the first phase in the machine learning architecture. A second cardiac event in the ECG data is classified based on the classified first cardiac event and the feature set, using a second phase in the machine learning architecture. The second cardiac event overlaps at least partially in time with the first cardiac event. Further, a plurality of feature sets, corresponding to a plurality channels of ECG data, are generated using paths in a machine learning architecture. A cardiac event in the ECG data is classified using the machine learning architecture based on the plurality of feature sets.
(FR)
L'invention concerne des techniques de classification d'événements cardiaques dans des données d'électrocardiogramme (ECG). Un ensemble de caractéristiques est généré par une analyse de données d'ECG d'un patient au moyen d'une première phase dans une architecture d'apprentissage machine. Un premier événement cardiaque dans les données d'ECG est classifié sur la base de l'ensemble de caractéristiques, au moyen de la première phase dans l'architecture d'apprentissage machine. Un second événement cardiaque dans les données d'ECG est classifié sur la base du premier événement cardiaque classifié et de l'ensemble de caractéristiques, au moyen d'une seconde phase dans l'architecture d'apprentissage machine. Le second événement cardiaque chevauche au moins partiellement dans le temps le premier événement cardiaque. En outre, les ensembles d'une pluralité d'ensembles de caractéristiques, correspondant aux canaux d'une pluralité de canaux de données d'ECG, sont générés au moyen de trajets dans une architecture d'apprentissage machine. Un événement cardiaque dans les données d'ECG est classifié à l'aide de l'architecture d'apprentissage machine sur la base des ensembles de la pluralité d'ensembles de caractéristiques.
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