Signal analysis of physiological recordings for decision support and outcome prediction in the paediatric intensive care unit

在该项目的3年期间将调查的核心研究课题是开发信号处理和潜在的机器学习算法，用于分析儿科重症监护病房床边收集的神经和心肺信号。这将包括清理信号的方法，获得相关特征，并最终预测结果。作为该项目的一部分，主要研究课题将最有可能包括：1。信号处理算法，确保从监测信号中成功提取数据。2.机器学习算法将尝试识别数据中的模式并预测生理效应，以支持临床决策。

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.