Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
ObjectiveTo systematically evaluate the methodological quality and predictive performance of acute kidney injury (AKI) prediction models following coronary artery bypass grafting (CABG), aiming to identify reliable tools for clinical practice and provide evidence-based guidance for developing higher-quality models in future. MethodsA systematic literature search was conducted across CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, EMbase, and Cochrane Library databases from inception to October 2025. Two independent reviewers screened studies, extracted data, and performed prediction model risk of bias assessment. Qualitative synthesis was followed by meta-analysis using STATA 15.0 software. ResultsA total of 21 studies involving 55 prediction models were included. The majority of the studies demonstrated good applicability, but exhibited high overall risk of bias. The models showed favorable discriminative ability, with areas under the receiver operating characteristic curves ranging from 0.707 to 0.958 in training cohorts, and a pooled area under the curve of 0.79 [95%CI (0.76, 0.82)]. The area under the receiver operating characteristic curve in the validation set ranged from 0.55 to 0.90, with a pooled area under the curve of 0.80 [95%CI (0.78, 0.81)]. Most models were presented as Nomograms. Common predictors included age, serum creatinine, estimated glomerular filtration rate, hemoglobin, uric acid, cardiopulmonary bypass, and intra-aortic balloon pump. ConclusionCurrent prediction models demonstrate satisfactory discrimination performance but are limited by single-center development, insufficient external validation, and methodological biases. Future multicenter prospective studies should optimize variable processing and model validation strategies to enhance clinical applicability and generalizability of predictive tools.
The study of neuronal activity with low frequency has shown an increasing interest for its greater stability and reliability recent years. One challenge in analyzing this kind of activity is to find similarities and differences between signals efficiently and effectively. The traditional analysis methods, such as short-time Fourier transform, are easily obscured by background noises and often involve a large number of parameters. Therefore, this paper introduces a novel time-frequency analysis method based on wavelet transformation and half-ellipsoid modeling to extract instantaneous frequency and instantaneous phase information. This method overcomes some shortcomings of conventional time-frequency analysis. In this method, wavelet transformation is used to provide high-level representations of raw signals, and parsimonious half-ellipsoid models are used to extract changes in time domain and frequency domain of neural recordings. The method was validated to local field potentials (LFPs) of olfactory bulb of anesthetized rats during three different odor stimuli. The results suggested that this method could detect odor-relevant features from olfactory signals with large variability. The Odors then were classified with support vector machine (SVM) algorithm and the classification accuracy reached 79.4%.
