With the intensification of global aging trends and the continuous rise in the incidence of chronic diseases, the demand for health monitoring and early intervention has become increasingly urgent. Owing to their non-invasive nature, portability, and comfort, flexible wearable sensors have emerged as a key technology driving the development of personalized healthcare. Starting from specific application scenarios in health monitoring, this article systematically reviews recent research advances in flexible sensors within the healthcare field. Firstly, it outlines the design fundamentals of flexible sensors. This is followed by a focused analysis of their specific applications in monitoring vital signs, biochemical markers, as well as motion and neural activities, along with an in-depth exploration of the clinical significance, technical challenges, and targeted solutions in different scenarios. Finally, the current technical bottlenecks and clinical challenges are summarized, and an outlook on the future development of health monitoring systems is provided. This review aims to provide a systematic reference for the deep integration of flexible electronics technology and medicine.
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive impairment, with the predominant clinical diagnosis of spatial working memory (SWM) deficiency, which seriously affects the physical and mental health of patients. However, the current pharmacological therapies have unsatisfactory cure rates and other problems, so non-pharmacological physical therapies have gradually received widespread attention. Recently, a novel treatment using 40 Hz light flicker stimulation (40 Hz-LFS) to rescue the cognitive function of model animals with AD has made initial progress, but the neurophysiological mechanism remains unclear. Therefore, this paper will explore the potential neural mechanisms underlying the modulation of SWM by 40 Hz-LFS based on cross-frequency coupling (CFC). Ten adult Wistar rats were first subjected to acute LFS at frequencies of 20, 40, and 60 Hz. The entrainment effect of LFS with different frequency on neural oscillations in the hippocampus (HPC) and medial prefrontal cortex (mPFC) was analyzed. The results showed that acute 40 Hz-LFS was able to develop strong entrainment and significantly modulate the oscillation power of the low-frequency gamma (lγ) rhythms. The rats were then randomly divided into experimental and control groups of 5 rats each for a long-term 40 Hz-LFS (7 d). Their SWM function was assessed by a T-maze task, and the CFC changes in the HPC-mPFC circuit were analyzed by phase-amplitude coupling (PAC). The results showed that the behavioral performance of the experimental group was improved and the PAC of θ-lγ rhythm was enhanced, and the difference was statistically significant. The results of this paper suggested that the long-term 40 Hz-LFS effectively improved SWM function in rats, which may be attributed to its enhanced communication of different rhythmic oscillations in the relevant neural circuits. It is expected that the study in this paper will build a foundation for further research on the mechanism of 40 Hz-LFS to improve cognitive function and promote its clinical application in the future.
With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer’s disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of information, and realize the imitation of brain information processing. However, its hardware is an important way to realize its powerful computing ability, and it is also a challenging technical problem. The memristor currently is the electronic devices that functions closest to the neuron synapse, and able to respond to spike voltage in a highly similar spike timing dependent plasticity (STDP) mechanism with a biological brain, and has become a research hotspot to construct spiking neuron networks hardware circuit in recent years. Through consulting the relevant literature at home and abroad, this paper has made a thorough understanding and introduction to the research work of the spiking neuron networks based on the memristor in recent years.
This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Electroencephalogram (EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.
Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.
Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.
The possible influence of electromagnetic field (EMF) on the function of neural systems has been widely concerned. In this article, we intend to investigate the effects of long term power frequency EMF exposure on brain cognitive functions and it’s mechanism. The Sprague-Dawley (SD) rats were randomly divided into 3 groups: the rats in EMF Ⅰ group were placed in the 2 mT power frequency EMF for 24 days. The rats in EMF Ⅱ group were placed in the 2 mT power frequency EMF for 48 days. The rats in control group were not exposed to the EMF. Then, the 16 channel local field potentials (LFPs) were recorded from rats’ prefrontal cortex (PFC) in each group during the working memory (WM) tasks. The causal networks of LFPs were also established by applying the directed transfer function (DTF). Based on that, the differences of behavior and the LFPs network connection patterns between different groups were compared in order to investigate the influence of long term power frequency EMF exposure on working memory. The results showed the rats in the EMF Ⅱ group needed more training to reach the task correction criterion (over 80%). Moreover, the causal network connection strength and the global efficiency of the rats in EMF Ⅰ and EMF Ⅱ groups were significantly lower than the corresponding values of the control group. Meanwhile, significant differences of causal density values were found between EMF Ⅱ group and the other two groups. These results indicate that long term exposure to 2 mT power frequency EMF will reduce the connection strength and the information transfer efficiency of the LFPs causal network in the PFC, as well as the behavior performance of the rats. These results may explain the effect of EMF exposure on working memory from the view of neural network connectivity and provide a support for further studies on the mechanism of the effect of EMF on cognition.
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that has been paid attention to with increasing interests as a therapeutic neural rehabilitative tool. Studies confirmed that high-frequency rTMS could improve the cognitive performance in behavioral test as well as the excitability of the neuron in animals. This study aimes to investigate the effects of rTMS on the cognition and neuronal excitability of Kunming mice during the natural aging. Twelve young mice, 12 adult mice, and 12 aged mice were used, and each age group were randomly divided into rTMS group and control group. rTMS-treated groups were subjected to high-frequency rTMS treatment for 15 days, and control groups were treated with sham stimulation for 15 days. Then, novel object recognition and step-down tests were performed to examine cognition of learning and memory. Whole-cell patch clamp technique was used to record and analyze resting membrane potential, action potential (AP), and related electrical properties of AP of hippocampal dentate gyrus (DG) granule neurons. Data analysis showed that cognition of mice and neuronal excitability of DG granule neurons were degenerated significantly as the age increased. Cognitive damage and degeneration of some electrical properties were alleviated under the condition of high-frequency rTMS. It may be one of the mechanisms of rTMS to alleviate cognitive damage and improve cognitive ability by changing the electrophysiological properties of DG granule neurons and increasing neuronal excitability.