The purpose of this study is to investigate the change of the whole brain event-related potentials(P300) in normal brain aging based on N-back cognitive tasks. The P300 of 15 normal young people and 10 normal old people were evaluated based on N-back cognitive tasks and analyzed. The results showed that the P300 latency of old people was longer in whole brain than young people, and amplitude was increased in the frontal-central region, while significantly increased in the pre-frontal region in the same load cognitive tasks. With the cognitive task load increasing, the amplitude of old people in high-load task was higher in the whole brain than that in low-load task, mainly in in the frontal region, but the difference was not statistically significant. The latency in the high-load task was shorter in the frontal-central region of right brain than the low-load task, and the difference was statistically significant. Thus, P300 showed that the normal brain aging process is mainly reflected in the pre-frontal region, and the high-load cognitive task could better reflect the change of brain function compared with the low-load cognitive task. The finding is of revelatory meaning for diagnosis of early dementia in patients.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.