Methods and systems for identifying presence of abnormal heart sounds of a subject
US12016705B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2020 |
| Grant date | Jun 25, 2024 |
| Priority date | — |
| Expiry date | Apr 27, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/10
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
The disclosure generally relates to methods and systems for identifying presence of abnormal heart sounds from heart sound signals of a subject being monitored. Conventional Artificial intelligence (AI) based abnormal heart sounds detection models with supervised learning requires a substantial amount of accurate training datasets covering all heart disease types for the training, which is quiet challenging. The present methods and systems solve the problem solves the problem of identifying presence of the abnormal heart sounds using an efficient semi-supervised learning model. The semi-supervised learning model is generated based on probability distribution of spectrographic properties obtained from heart sound signals of healthy subjects. A Kullback-Leibler (KL) divergence between a predefined Gaussian distribution and an encoded probability distribution of the semi-supervised learning model is determined as an anomaly score for identifying the abnormal heart sounds.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.