| Summary: | 碩士 === 元智大學 === 機械工程研究所 === 89 === In the neurosurgical intensive care unit, medical doctors often deal patients with severe head injuries. There are many researches point out that the immediate direct relationships between the increasing of intracranial pressure and the patients’ complications. Therefore, this has become very important that the diagnosis of intracranial pressure about the patient’s therapy. However, for measuring the intracranial pressure, it must use the invasive pressure sensor, which is usually depending on the surgeon putting into the patient’s cranial space. Not only it is a heavy burden for a surgeon, but also it increased lots of risks to the patients. For this reason, we hope that we can establish a way in place of the invasive measuring intracranial pressure by the means of non-invasive, using an intelligence analysis method. This would be a great worth issue for neurosurgery.
The major goal of this thesis consists in using neural network to build up a patient’s intracranial pressure model, by the means of measuring the non-invasive physiological signals from patients. But, due to the intracranial pressure is usually affected by many factors both predictable and unpredictable; it is not comprehensive if we only depend on the characters that neural networks process non-linear problems. Therefore, this thesis is based on the structure of recurrent network, to develop a whole new neural network algorithm that calls a simple recurrent network through time（SRNTT）. Thereafter we compared with the time delay factor to this built structure, and then evaluated the benefit of this learning in regards to different models. Finally, we use this SRNTT model to build the patient model for ICP. In order to explain the model’s output more thoroughly, doctors’ clinical experience will be included as well.
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