Systems and methods for electrocardiogram diagnosis using deep neural networks and rule-based systems
US11571161B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 8, 2019 |
| Grant date | Feb 7, 2023 |
| Priority date | — |
| Expiry date | Aug 24, 2040 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA61B5/7267
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
Methods and systems are provided for automatically diagnosing an electrocardiogram (ECG) using a hybrid system comprising a rule-based system and one or more deep neural networks. In one embodiment, by mapping ECG data to a plurality of features using a convolutional neural network, mapping the plurality of features to a preliminary diagnosis using a decision network, and determining a diagnosis based on the ECG data and the preliminary diagnosis using the rule-based system, a more accurate diagnosis may be determined. In another example, by incorporating both a rule-based system and one or more deep neural networks into the hybrid system, the hybrid system may be more easily adapted for use in various contexts/communities, as the one or more deep learning networks may be trained using context/community specific ECG data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.