Interpretable neural networks for cuffless blood pressure estimation
US12165052B2 · kind B2 · utility
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18Claims
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Key dates
| Filing date | Jul 31, 2020 |
| Grant date | Dec 10, 2024 |
| Priority date | — |
| Expiry date | May 21, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/70
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In some examples, an individually-pruned neural network can estimate blood pressure from a seismocardiogram (SCG). In some examples, a baseline model can be constructed by training the model with SCG data and blood pressure measurement from a plurality of subjects. One or more filters (e.g., the filters in the top layer of the network) can be ranked by separability, which can be used to prune the model for each unseen user that uses the model thereafter, for example. In some examples, individuals can use individually-pruned models to calculate blood pressure using SCG data without corresponding blood pressure measurements.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.