Accumulation of amyloid-β is a key neuropathological feature in brain of Alzheimer’s disease patients. Alterations in cerebral haemodynamics, such as arterial impulse propagation driving the (peri)vascular CSF flux, predict future Alzheimer’s disease progression. We now present a non-invasive method to quantify the three-dimensional propagation of cardiovascular impulses in human brain using ultrafast 10 Hz magnetic resonance encephalography. This technique revealed spatio-temporal abnormalities in impulse propagation in Alzheimer’s disease. The arrival latency and propagation speed both differed in patients with Alzheimer’s disease. Our mapping of arterial territories revealed Alzheimer’s disease-specific modifications, including reversed impulse propagation around the hippocampi and in parietal cortical areas. The findings imply that pervasive abnormality in (peri)vascular CSF impulse propagation compromises vascular impulse propagation and subsequently glymphatic brain clearance of amyloid-β in Alzheimer’s disease.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, which particularly occurs in the elderly individuals with heart disease. Though AF is often asymptomatic during normal activities, it has huge potential risks for stroke and other severe diseases. Thus, early detection of AF has great importance in the field of public health. Currently, electrocardiography (ECG) is the commonly used measure for the diagnosis of AF, which presents the irregular rhythm of waveform for AF patients. However, the measurement of the ECG signal requires special medical acquisition devices, which are not comfortable for practical monitoring in daily life. In this paper, we explore a very promising algorithm to detect AF from remote face videos by analyzing the color variations of face skin. The main challenge is that the current remote photoplethysmography (rPPG) technique is rather immature, which causes difficulty in extracting accurate pulse signals for describing the cardiac rhythm. To solve this problem, we first utilize various rPPG algorithms to capture pulse rhythms from different regions on the face video. We then investigate biomedical statistical methods to extract suitable features from each pulse signal. Due to the imprecision of video-extracted pulse signals, some traditional physiological features may lose their utility since they were originally proposed for ECG signals. Furthermore, some of them are very susceptible to the influence of noise. Thus, we propose a feature fusion algorithm to select and combine reasonable information from multiple physiological features, which aims to preserve the discriminability of detecting AF in the presence of the noise and outlier disturbances. The experimental results on a real-world database demonstrate the effectiveness of the proposed method in providing useful information for AF detection.

Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.