Systematic review with meta-analysis: Significant histological changes among treatment-naïve chronic hepatitis B patients with normal alanine aminotransferase levels by different criteria.

BACKGROUND: The upper limits of normal (ULNs) of ALT are not consistent across the major international guidelines which may affect the eligibility for antiviral therapy for chronic hepatitis B (CHB). AIM: To estimate the proportions of histological changes among treatment-naïve patients with CHB within differently defined ALT ULNs. METHODS: We searched PubMed and Embase up to May 15th, 2023, to identify studies of treatment-naïve CHB patients with liver biopsies. We pooled proportions of moderate to severe necroinflammation, significant fibrosis, and cirrhosis in those patients within different ALT ULNs by using random-effect models. RESULTS: We included 23 studies with 4010 participants. Within ALT ULN at 40 IU/L, the pooled proportions of moderate to severe necroinflammation, significant fibrosis, and cirrhosis were 33% (95% CI: 26%-42%), 32% (95% CI: 27%-38%), and 3% (95% CI: 1%-5%), respectively. Within ALT ULN at 30 IU/L for men and 19 IU/L for women, the pooled proportion of significant fibrosis remained at 30% (95% CI: 25%-34%; 432 participants). However, it was 21% (95% CI: 11%-37%; 361 participants) even in those within ALT ULN at 20 IU/L. Subgroup analyses suggested a significantly higher proportion of significant fibrosis among studies with prospective design or enrolled patients' mean age >35 or >40 years. CONCLUSIONS: Significant histological changes occurred in approximately 1/3 of treatment-naïve CHB patients within ALT ULN at 40 IU/L, whereas the proportion of significant fibrosis was approximately 1/5 even in those within ALT ULN at 20 IU/L.

Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network.

The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantial amount of prior knowledge to extract degraded features. A large number of recurrent neural networks (RNNs) have been applied to RUL, but their shortcomings of long-term dependence and inability to remember long-term historical information can result in low RUL prediction accuracy. To address this limitation, this paper proposes an RUL prediction method based on adaptive shrinkage processing and a temporal convolutional network (TCN). In the proposed method, instead of performing the feature extraction to preprocess the original data, the multi-channel data are directly used as an input of a prediction network. In addition, an adaptive shrinkage processing sub-network is designed to allocate the parameters of the soft-thresholding function adaptively to reduce noise-related information amount while retaining useful features. Therefore, compared with the existing RUL prediction methods, the proposed method can more accurately describe RUL based on the original historical data. Through experiments on a PHM2012 rolling bearing data set, a XJTU-SY data set and comparison with different methods, the predicted mean absolute error (MAE) is reduced by 52% at most, and the root mean square error (RMSE) is reduced by 64% at most. The experimental results show that the proposed adaptive shrinkage processing method, combined with the TCN model, can predict the RUL accurately and has a high application value.

A robust principal component analysis algorithm for EEG-based vigilance estimation.

Feature dimensionality reduction methods with robustness have a great significance for making better use of EEG data, since EEG features are usually high-dimensional and contain a lot of noise. In this paper, a robust principal component analysis (PCA) algorithm is introduced to reduce the dimension of EEG features for vigilance estimation. The performance is compared with that of standard PCA, L1-norm PCA, sparse PCA, and robust PCA in feature dimension reduction on an EEG data set of twenty-three subjects. To evaluate the performance of these algorithms, smoothed differential entropy features are used as the vigilance related EEG features. Experimental results demonstrate that the robustness and performance of robust PCA are better than other algorithms for both off-line and on-line vigilance estimation. The average RMSE (root mean square errors) of vigilance estimation was 0.158 when robust PCA was applied to reduce the dimensionality of features, while the average RMSE was 0.172 when standard PCA was used in the same task.

Differential entropy feature for EEG-based vigilance estimation.

This paper proposes a novel feature called differential entropy for EEG-based vigilance estimation. By mathematical derivation, we find an interesting relationship between the proposed differential entropy and the existing logarithm energy spectrum. We present a physical interpretation of the logarithm energy spectrum which is widely used in EEG signal analysis. To evaluate the performance of the proposed differential entropy feature for vigilance estimation, we compare it with four existing features on an EEG data set of twenty-three subjects. All of the features are projected to the same dimension by principal component analysis algorithm. Experiment results show that differential entropy is the most accurate and stable EEG feature to reflect the vigilance changes.

A novel method for EOG features extraction from the forehead.

We have shown that the slow eye movements extracted from electrooculogram (EOG) signals can be used to estimate human vigilance in our previous work. However, the traditional method for recording EOG signals is to place the electrodes near the eyes of subjects. This placement is inconvenient for users in real-world applications. This paper aims to find a more practical placement for acquiring EOG signals for vigilance estimation. Instead of placing the electrodes near the eyes, we place them on the forehead. We extract EOG features from the forehead EOG signals using both independent component analysis and support vector machines. The performance of our proposed method is evaluated using the correlation coefficients between the forehead EOG signals and the traditional EOG signals. The results show that a correlation of 0.84 can be obtained when the users make 14 different face movements and for merely eye movements it reaches 0.93.

Evidence of rapid gender processing revealed by ERSP.

In this research, we used EEG signals to analyze gender processing with the ERSP method. Not only facial images, but also images of clothing and shoes, were used. We applied the ICA method to obtain a gender-related component which appeared quite significant in the majority of electrode sites for the occipital lobe. This showed differences of energy between the two genders, even for the clothing and shoe images. Our results indicate that not only facial gender processing, but also a gender discrimination task for objects influences the energy of EEGs from 50 ms after the onset of stimuli at all frequencies, especially lower band. This provides convincing evidence for rapidity of gender processing.

Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine.

Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.

Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning.

For many human machine interaction systems, to ensure work safety, the techniques for continuously estimating the vigilance of operators are highly desirable. Up to now, various methods based on electroencephalogram (EEG) are proposed to solve this problem. However, most of them are static methods and are based on supervised learning strategy. The main deficiencies of the existing methods are that the label information is hard to get and the time dependency of vigilance changes are ignored. In this paper, we introduce the dynamic characteristics of vigilance changes into vigilance estimation and propose a novel model based on linear dynamical system and manifold learning techniques to implement off-line and online vigilance estimation. In this model, both spatial information of EEG and temporal information of vigilance changes are used. The label information what we need is merely to know which EEG indices are important for vigilance estimation. Experimental results show that the mean off-line and on-line correlation coefficients between estimated vigilance level and local error rate in second-scale without being averaged are 0.89 and 0.83, respectively.

Vigilance estimation by using electrooculographic features.

This study aims at using electrooculographic (EOG) features, mainly slow eye movements (SEM), to estimate the human vigilance changes during a monotonous task. In particular, SEMs are first automatically detected by a method based on discrete wavelet transform, then linear dynamic system is used to find the trajectory of vigilance changes according to the SEM proportion. The performance of this system is evaluated by the correlation coefficients between the final outputs and the local error rates of the subjects. The result suggests that SEMs perform better than rapid eye movements (REM) and blinks in estimating the vigilance. Using SEM alone, the correlation can achieve 0.75 for off-line, while combined with a feature from blinks it reaches 0.79.

Dynamic clustering for vigilance analysis based on EEG.

Electroencephalogram (EEG) is the most commonly studied signal for vigilance estimation. Up to now, many researches mainly focus on using supervised learning methods for analyzing EEG data. However, it is hard to obtain enough labeled EEG data to cover the whole vigilance states, and sometimes the labeled EEG data may be not reliable in practice. In this paper, we propose a dynamic clustering method based on EEG to estimate vigilance states. This method uses temporal series information to supervise EEG data clustering. Experimental results show that our method can correctly discriminate between the wakefulness and the sleepiness for every 2 seconds through EEG, and can also distinguish two other middle states between wakefulness and sleepiness.