Epilepsy seizure detection and prediction using techniques such as deep learning methods
US11219405B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 1, 2018 |
| Grant date | Jan 11, 2022 |
| Priority date | — |
| Expiry date | Oct 5, 2038 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA61B2562/0219
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction. The streams may be from multiple people and applied to an individual person.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.