Probabilistic model-based time-series forecasting neural networks and related applications to biosignal forecasting
Description: We proposed mathematical models that can represent biological signals, which are electrical signals that can be measured from the human body, and applied them to the analysis of actual data. For example, we modeled myoelectric signals, which are electrical signals of muscles, and applied them to the analysis of muscle strength. We also proposed models for electrocardiogram and electroencephalogram and applied them to signal classification. Furthermore, we developed neural networks based on probabilistic models, and applied them to data classification and time series forecasting. In addition, we constructed large medical datasets such as biological signals of a pregnant woman called cardiotocography and endoscopic images, and applied them to fetal condition prediction and organ classification.
Amount: ¥4,160,000
Funding type: fellowship
Funder scheme: Grant-in-Aid for Young Scientists (B)
Start date: 2017-04-01
End date: 2021-03-31
Investigator: Hideaki Hayashi
Institution: Kyushu University
Funded outputs count: 7
Funder award ID: 17K12752
OpenAlex ID: G2937962139