Invention:
This invention is a deep learning algorithm system that can incorporate human-interpretable explanations in its training process for the analysis of ECG signals. This method takes into account classification loss, the influence of both important and unimportant features to model prediction, and user feedback. This invention is geared toward real-time ECG signals to provide clinical decision support to health care providers in the form of a web or mobile application. This technology can enable a faster and more reliable method of reading ECGs and help screen and identify important image features that require expert scrutiny.
Background:
Deep learning is an advanced type of artificial neural networks that has gained significant attention as a subset of machine learning with significant investments in research over the last few years. This technology largely emulates the human brain by learning and solving complex problems, which may be applied to the interpretation of ECG signals where resources in trained ECG experts and/or health care reimbursement are limited. Deep learning is currently the fastest growing segment of artificial intelligence due to its ability to extract information from unstructured data.
Applications:
- ECG image classification
- Medical imaging classification
Advantages:
- Capable of multiple classifications
- Increased accuracy and sensitivity
- Deep learning improved with human feedback
