Ensemble Back-propagation Neural Network Applied to brain death index prediction for the patients with severe head injury

• Main Authors: Kai-Yuan Cheng, 鄭凱元
• Other Authors: 謝建興
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/11787257805469010862

碩士 === 元智大學 === 機械工程學系 === 96 === 　　The concept of organ donation has already been accepted by people gradually in recent years so the judicial brain death determination process becomes very important. Clinically, patients with irreversible apnoeic coma (IAC) will be considered legally as brain death based on a judicial process, but this process can only be applied to people who had already signed the letter of consent to donate organs. Besides, the judicial process is also very lengthy. This study tried to find out an easier way to diagnose the prognosis of the patients with severe head injury, and offer the medical staffs other information to determine brain death. 　　Medical doctors depend on their experience to diagnose patients’ prognosis by observing the variation of different physiological signals. We tried to find out this relationship between the signals’ variation and doctors’ determination process. Heart rate variability (HRV) is the most common noninvasive physiological signal. It’s also a rich signal which contains a lot of information of human body, and probably the most investigated and readily assessable measure of autonomic nerves’ function. Therefore, we designed a two-stage experiment to achieve our purpose. For the first stage, the model is an expert system designed to mimic the determining process of medical doctors. We chose ten most important physiological signals to be the analyzing data in the first stage. But in the second stage, we only focused on analyzing the heart rate signal. The technique of artificial neural networks (ANN) and empirical mode decomposition (EMD) has been applied to construct the prediction model of brain death index (BDI). The multi-layer perception (MLP) and ensemble neural networks are chosen to be the network type of BDI model. This model can provide medical staffs a reference index to evaluate the status of IAC and brain death patients.