The Decision-Making System for Alzheimer’s Patients by Understanding Ability Test from Physiological Signals

Abstract

In recent years, end-of-life medical care and property allocation for Alzheimer’s patients have received attention. Due to the declining birthrate and aging society, there will be more and more patients with Alzheimer’s disease in the future. However, many patients with Alzheimer’s disease are unable to make decisions as they wish, and people around them are also pressured to make decisions for them. Therefore, this study uses physiological signals to identify patients with Alzheimer’s disease to judge understanding degree. At the same time, judge the emotional changes to help them better to express and live as they wish at the end of life. In this paper, we have improved the indistinctness of the unclear spectrogram of Brain Waves (EEG) used by the Short Time Fourier Transform (STFT), and improved the Wavelet Transform (WT) is better than STFT. We let the experimenter to solve the logical graphic questions in a specified time, collect the EEG of the experimenters and analyze it. Comparing the spectrogram of EEG when the experimenter gets the answer and judge the understood degree with CNN of Deep-learning.