Signal analysis of physiological recordings for decision support and outcome prediction in the paediatric intensive care unit
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Engineering
Abstract
The core research topic that will be investigated during the 3-year period of this project is the development of signal processing and potentially machine learning algorithms for the analysis of neurological and cardiorespiratory signals collected at the bedside in paediatric intensive-care units. This will include methods to clean the signals, obtain relevant features, and finally predict outcomes.
The main research topics investigated as part of this project will most likely include:
1. Signal processing algorithms that ensure the successful extraction of data from monitoring signals.
2. Machine learning algorithms that would try to identify patterns in the data and predict physiological effects in order to support clinical decision making.
The main research topics investigated as part of this project will most likely include:
1. Signal processing algorithms that ensure the successful extraction of data from monitoring signals.
2. Machine learning algorithms that would try to identify patterns in the data and predict physiological effects in order to support clinical decision making.
People |
ORCID iD |
| Evangelos Kafantaris (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509644/1 | 30/09/2016 | 29/09/2021 | |||
| 2107746 | Studentship | EP/N509644/1 | 31/08/2018 | 29/09/2022 | Evangelos Kafantaris |
| NE/W503149/1 | 31/03/2021 | 30/03/2022 | |||
| 2107746 | Studentship | NE/W503149/1 | 31/08/2018 | 29/09/2022 | Evangelos Kafantaris |