Alzheimer’s disease (AD) is the most common degenerative disease of the nervous system. Studies have found that the 40 Hz pulsed magnetic field has the effect of improving cognitive ability in AD, but the mechanism of action is not clear. In this study, APP/PS1 double transgenic AD model mice were used as the research object, the water maze was used to group dementia, and 40 Hz/10 mT pulsed magnetic field stimulation was applied to AD model mice with different degrees of dementia. The behavioral indicators, mitochondrial samples and electrocardiogram signals of hippocampal CA1 region were collected from each group, and the effects of 40 Hz pulsed magnetic field on mouse behavior, mitochondrial kinetic indexes and heart rate variability (HRV) parameters were analyzed. The results showed that compared with the AD group, the loss of mitochondrial crest structure was alleviated and the mitochondrial dynamics related indexes were significantly improved in the AD + stimulated group (P<0.001), sympathetic nerve excitation and parasympathetic nerve inhibition were improved, and the spatial cognitive memory ability of mice was significantly improved (P<0.05). The preliminary results of this study show that 40 Hz pulsed magnetic field stimulation can improve the mitochondrial structure and mitochondrial kinetic homeostasis imbalance of AD mice, and significantly improve the autonomic neuromodulation ability and spatial cognition ability of AD mice, which lays a foundation for further exploring the mechanism of ultra-low frequency magnetic field in delaying the course of AD disease and realizing personalized neurofeedback therapy for AD.
In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.