[A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals].

Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.

Pressure-flow curve derived from coronary CT angiography for detection of significant hemodynamic stenosis.

OBJECTIVES: Coronary CT angiography (cCTA) has been used to non-invasively assess both the anatomical and hemodynamic significance of coronary stenosis. The current study investigated a new CFD-based method of evaluating pressure-flow curves across a stenosis to further enhance the diagnostic value of cCTA imaging. METHODS: Fifty-eight patients who underwent both cCTA imaging and invasive coronary angiography (ICA) with fractional flow reserve (FFR) within 2 weeks were enrolled. The pressure-flow curve-derived parameters, viscous friction (VF) and expansion loss (EL), were compared with conventional cCTA parameters including percent area stenosis (AS) and minimum lumen area (MLA) by receiver operating characteristic (ROC) curve analysis. FFR ≤ 0.80 was used to indicate ischemia-causing stenosis. Correlations between FFR and other measurements were calculated by Spearman's rank correlation coefficient (rho). RESULTS: Sixty-eight stenoses from 58 patients were analyzed. VF, EL, and AS were significantly larger in the group of FFR ≤ 0.8 while smaller MLA values were observed. The ROC-AUC of VF (0.91, 95% CI 0.81-0.96) was better than that of AS (change in AUC (ΔAUC) 0.27, p < 0.05) and MLA (ΔAUC 0.17, p < 0.05), and ROC-AUC of EL (0.90, 95%CI 0.80-0.96) was also better than that of AS (ΔAUC 0.26, p < 0.05) and MLA (ΔAUC 0.16, p < 0.05). FFR values correlated well with VF (rho = - 0.74 (95% CI - 0.83 to - 0.61, p < 0.0001) and EL (rho = - 0.74 (95% CI - 0.83 to - 0.61, p < 0.0001). CONCLUSION: Pressure-flow curve-derived parameters enhance the diagnostic value of cCTA examination. KEY POINTS: • Pressure-flow curve derived from cCTA can assess coronary lesion severity. • VF and EL are superior to cCTA alone for indicating ischemic lesions. • Pressure-flow curve derived from cCTA may assist in clinical decision-making.

[Analysis of imagery motor effective networks based on dynamic partial directed coherence].

The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time-frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 ( P = 0.007) and ROI3 ( P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.

A new CFD based non-invasive method for functional diagnosis of coronary stenosis.

BACKGROUND: Accurate functional diagnosis of coronary stenosis is vital for decision making in coronary revascularization. With recent advances in computational fluid dynamics (CFD), fractional flow reserve (FFR) can be derived non-invasively from coronary computed tomography angiography images (FFRCT) for functional measurement of stenosis. However, the accuracy of FFRCT is limited due to the approximate modeling approach of maximal hyperemia conditions. To overcome this problem, a new CFD based non-invasive method is proposed. METHODS: Instead of modeling maximal hyperemia condition, a series of boundary conditions are specified and those simulated results are combined to provide a pressure-flow curve for a stenosis. Then, functional diagnosis of stenosis is assessed based on parameters derived from the obtained pressure-flow curve. RESULTS: The proposed method is applied to both idealized and patient-specific models, and validated with invasive FFR in six patients. Results show that additional hemodynamic information about the flow resistances of a stenosis is provided, which cannot be directly obtained from anatomy information. Parameters derived from the simulated pressure-flow curve show a linear and significant correlations with invasive FFR (r > 0.95, P < 0.05). CONCLUSION: The proposed method can assess flow resistances by the pressure-flow curve derived parameters without modeling of maximal hyperemia condition, which is a new promising approach for non-invasive functional assessment of coronary stenosis.

Theoretical Model of Coronary Blood Flow Regulation: Role of Myocardium Compressive Forces.

OBJECTIVE: Auto-regulatory reserve of coronary blood flow is nonuniformly distributed across the ventricular wall. MCF are thought to play an important role in determining the transmural distribution of myocardium blood flow. Here, impacts of MCF on coronary flow regulation are analyzed using a theoretical model. METHODS: Coronary microvessels at various depths in the ventricular wall are represented by parallel segments. Nine vessel regions are connected in series to represent one parallel segment, which includes four vasoactive regions regulated by the wall tension, the shear stress and the metabolic demand. The nonuniform distribution of MCF is modeled and its effects on coronary flow regulation are taken into consideration by using a modified tension model and a vessel collapse model. Flow regulation behaviors in both normal and obstructed coronary circulation are simulated. RESULTS: Model-predicted auto-regulatory curve is shifted to the high pressure region by including the effect of MCF. Model-predicted flow distributions in obstructed coronary circulation show that severe stenosis in coronary artery would first impede myocardial blood flow in subendocardial layer. CONCLUSIONS: The model results indicate that MCF plays an important role in coronary flow regulation and also in determining the transmural distribution of myocardium blood flow.

Computation of hemodynamics in tortuous left coronary artery: a morphological parametric study.

Coronary tortuosity (CT) would alter the local wall shear stress (WSS) and may become a risk factor for atherosclerosis. Here we performed a systematic computational study to relate CT morphological parameters to abnormal WSS, which is a predisposing factor to the formation of atherosclerotic lesions. Several idealized left coronary artery (LCA) models were created to conduct a series of morphological parametric studies, in which we concentrate on three specific morphological parameters, the center line radius (CLR), the bend angle (BA), and the length between two adjust bends (LBB). The time averaged WSS (TAWSS), the oscillatory shear index (OSI), and the time averaged WSS gradient (WSSGnd) were explored by using the computational fluid dynamics (CFD) method, in order to determine susceptible sites for the onset of early atherosclerosis. In addition, two realistic LCA models were reconstructed to further validate the finding's credibility. The CLR and LBB had great impact on the distributions of WSS-derived parameters, while the BA had minor impact on the hemodynamic of the tortuous arteries. Abnormal regions with low TAWSS (TAWSS < 0.5 Pa), high OSI (OSI > 0.1) and high WSSGnd (WSSGnd > 8) were observed at the inner wall of bend sections in the models with small CLR or small LBB. These findings were also confirmed in the realistic models. Severe CT with small CLR or LBB would lead to the formation of abnormal WSS regions at the bend sections and providing these regions with favorable conditions for the onset and/or progression of atherosclerosis.

Computing pulsatile blood flow of coronary artery under incomplete boundary conditions.

BACKGROUND: Accurate measurement of pulsatile blood flow in the coronary arteries enables coronary wave intensity analysis, which can serve as an indicator for assessing coronary artery physiology and myocardial viability. Computational fluid dynamics (CFD) methods integrating coronary angiography images and fractional flow reserve (FFR) offer a novel approach for computing mean coronary blood flow. However, previous methods neglect the inertial effect of blood flow, which may have great impact on pulsatile blood flow calculation. To improve the accuracy of pulsatile blood flow calculation, a novel CFD based method considering the inertia term is proposed. METHODS: A flow resistance model based on Pressure-Flow vs.Time curves is proposed to model the resistance of the epicardial artery. The parameters of the flow resistance model can be fitted from the simulated pulsating flow rates and pressure drops of a specific mode. Then, pulsating blood flow can be calculated by combining the incomplete pressure boundary conditions under pulsating conditions which are easily obtained in clinic. Through simulation experiments, the effectiveness of the proposed method is validated in idealized and reconstructed 3D model of coronary artery. The impacts of key parameters for generating the simulated pulsating flow rates and pressure drops on the accuracy of pulsatile blood flow calculation are also investigated. RESULTS: For the idealized model, the previously proposed Pressure-Flow model has a significant leading effect on the computed blood flow waveform in the moderate model, and this leading effect disappears with the increase of the degree of stenosis. The improved model proposed in this paper has no leading effect, the root mean square error (RMSE) of the proposed model is low (the left coronary mode:≤0.0160, the right coronary mode:≤0.0065) for all simulated models, and the RMSE decreases with an increase of stenosis. The RMSE is consistently small (≤0.0217) as the key parameters of the proposed method vary in a large range. It is verified in the reconstructed model that the proposed model significantly reduces the RMSE of patients with moderate stenosis (the Pressure-Flow model:≤0.0683, the Pressure-Flow vs.Time model:≤0.0297), and the obtained blood flow waveform has a higher coincidence with the simulated reference waveform. CONCLUSIONS: This paper confirms that ignoring the effect of inertia term can significantly affect the accuracy of calculating pulsatile blood flow in moderate stenosis lesions, and the new method proposed in this paper can significantly improves the accuracy of calculating pulsatile blood flow in moderate stenosis lesions. The proposed method provides a convenient clinical method for obtaining pressure-synchronized blood flow, which is expected to facilitate the application of waveform analysis in the diagnosis of coronary artery disease.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2GRL) framework to model EEG signals as graph data. Specifically, MD 2GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.

Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.

A transfer learning-based feedback training motivates the performance of SMR-BCI.

Objective. Feedback training is a practical approach to brain-computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMRs). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation. However, few studies have taken into account the users' self-motivation as additional guidance for the cognitive process involved in BCI learning. In this study we tested a transfer learning (TL) feedback method designed to increase self-motivation by providing information about past performance.Approach. Electroencephalography (EEG) signals from the previous runs were affine transformed and displayed as points on the screen, along with the newly recorded EEG signals in the current run, giving the subjects a context for self-motivation. Subjects were asked to separate the feedback points for the current run under the display of the separability of prior training. We conducted a between-subject feedback training experiment, in which 24 healthy SMR-BCI naive subjects were trained to imagine left- and right-hand movements. The participants were provided with either TL feedback or typical cursor-bar (CB) feedback (control condition), for three sessions on separate days.Main results. The behavioral results showed an increased challenge and stable mastery confidence, suggesting that subjects' motivation grew as the feedback training went on. The EEG results showed favorable overall training effects with TL feedback in terms of the class distinctiveness and EEG discriminancy. Performance was 28.5% higher in the third session than in the first. About 41.7% of the subjects were 'learners' including not only low-performance subjects, but also good-performance subjects who might be affected by the ceiling effect. Subjects were able to control BCI with TL feedback with a higher performance of 60.5% during the last session compared to CB feedback.Significance. The present study demonstrated that the proposed TL feedback method boosted psychological engagement through the self-motivated context, and further allowed subjects to modulate SMR effectively. The proposed TL feedback method also provided an alternative to typical CB feedback.

Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT).

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1-30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.

LDER: a classification framework based on ERP enhancement in RSVP task.

Objective.Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications.Approach.To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework.Main results.Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain-computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes.Significance.As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.

Computing intracoronary blood flow rate under incomplete boundary conditions: Combing coronary anatomy and fractional flow reserve.

BACKGROUND: Accurate measurement of intracoronary blood flow rate is of great significance for the diagnosis of ischemic heart disease (IHD). Computational fluid dynamic (CFD) method, combining coronary angiography images and fractional flow reserve (FFR), provides a new way to calculate the mean flow rate. However, due to the incomplete boundary conditions obtained by FFR, side branches were ignored which was likely to have a significant impact on the accuracy. In this paper, a novel CFD based method for calculating the mean intracoronary flow rate under incomplete pressure boundary conditions was proposed, in order to improve the accuracy by including the side branches. METHODS: A pressure-flow curve based flow resistance model was employed to model resistance of the epicardial arteries. A series of steady flow simulations were performed to extract the parameters of the flow resistance model, which implicitly specified constraints for splitting flow between branches and thus enabled the mean intracoronary blood flow rate to be calculated in two or more branches under incomplete pressure boundary conditions. Simulation experiments were designed to validate the proposed method in both idealized and reconstructed 3D models of coronary branches, and the impact of the assumed coefficient of the Murray's Law for splitting flow between branches was also investigated. RESULTS: The mean percentage error of the proposed method was +2.05%±0.04% for idealized models and +2.24%±0.01% for reconstructed models, and it was much lower than that of the method ignoring side branches (+38.48%±10.45% for idealized models and +30.54%±6.12% for reconstructed models). When the assumed coefficient of the Murray's Law was inconsistent with the real blood flow condition, the percentage errors still maintained less than about 3.00%. CONCLUSIONS: The proposed method provided an easy and accurate way to measure the mean intracoronary flow rate and would facilitate the accurate diagnosis of IHD.

Why do people resist algorithms? From the perspective of short video usage motivations.

Algorithms embedded in media applications increasingly influence individuals' media practice and behavioral decisions. However, it is also important to consider how the influence of such algorithms can be resisted. Few studies have explored the resistant outcomes of the interactions with algorithms. Based on an affordance perspective, this study constructed a formation framework of algorithmic resistance in the context of short videos in China. Survey responses from 2,000 short video users to test the model. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were used for data analysis. The findings reveal two types of "moderate" resistance: avoidance and obfuscation. Specific needs, such as the motivations of peeking and escapism, are significantly related to perceived algorithmic affordance, which, in turn, encourages the tactics of avoidant and obfuscated resistance. The results provide new insights into the potential formation mechanisms of algorithmic resistance. The forms of resistance highlighted in the paper evolve alongside algorithms and have significant practical implications for users and platforms.

Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection.

Background: Rapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions. The fusion of human-computer vision can obtain complementary information, making it a promising way to become an efficient and general way to detect objects, especially in attention lapses. Methods: Dynamic probability integration (DPI) was proposed in this study to fuse human vision and computer vision. A novel basic probability assignment (BPA) method was included, which can fully consider the classification capabilities of different heterogeneous information sources for targets and non-targets and constructs the detection performance model for the weight generation based on classification capabilities. Furthermore, a spatial-temporal hybrid common spatial pattern-principal component analysis (STHCP) algorithm was designed to decode EEG signals in the RSVP task. It is a simple and effective method of distinguishing target and non-target using spatial-temporal features. Results: A nighttime vehicle detection based on the RSVP task was performed to evaluate the performance of DPI and STHCP, which is one of the conditions of attention lapses because of its decrease in visual information. The average AUC of DPI was 0.912 ± 0.041 and increased by 11.5, 5.2, 3.4, and 1.7% compared with human vision, computer vision, naive Bayesian fusion, and dynamic belief fusion (DBF), respectively. A higher average balanced accuracy of 0.845 ± 0.052 was also achieved using DPI, representing that DPI has the balanced detection capacity of target and non-target. Moreover, STHCP obtained the highest AUC of 0.818 ± 0.06 compared with the other two baseline methods and increased by 15.4 and 23.4%. Conclusion: Experimental results indicated that the average AUC and balanced accuracy of the proposed fusion method were higher than individual detection methods used for fusion, as well as two excellent fusion methods. It is a promising way to improve detection performance in RSVP tasks, even in abnormal conditions.

Research on online advertising attention evaluation decision based on the stability of delay differential equations and Hopf bifurcation analysis.

This paper uses the stability of the delay differential equation to study its impact on online advertising, helps analyze Hopf branch characteristics in a big data environment, helps companies make online advertising decisions, and maximizes the benefits of product sales. The thesis fully considers various factors such as advertising volume, advertising schedule, and advertising investment level, discusses the singularity types of the advertising delay differential equation, and gives the best decision for advertising investment.The stability of the time-lag differential equation studied in this paper is to study its impact on online advertising, to help analyze the Hopf branch characteristics in the big data environment, and to help companies make online advertising decisions. structure of this article is also from the amount of advertising, the time of advertising, Advertising investment level gradually expands with a certain degree of continuity.

An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training.

Brain-computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect.

Validation and Diagnostic Performance of a CFD-Based Non-invasive Method for the Diagnosis of Aortic Coarctation.

Purpose: The clinical diagnosis of aorta coarctation (CoA) constitutes a challenge, which is usually tackled by applying the peak systolic pressure gradient (PSPG) method. Recent advances in computational fluid dynamics (CFD) have suggested that multi-detector computed tomography angiography (MDCTA)-based CFD can serve as a non-invasive PSPG measurement. The aim of this study was to validate a new CFD method that does not require any medical examination data other than MDCTA images for the diagnosis of CoA. Materials and methods: Our study included 65 pediatric patients (38 with CoA, and 27 without CoA). All patients underwent cardiac catheterization to confirm if they were suffering from CoA or any other congenital heart disease (CHD). A series of boundary conditions were specified and the simulated results were combined to obtain a stenosis pressure-flow curve. Subsequently, we built a prediction model and evaluated its predictive performance by considering the AUC of the ROC by 5-fold cross-validation. Results: The proposed MDCTA-based CFD method exhibited a good predictive performance in both the training and test sets (average AUC: 0.948 vs. 0.958; average accuracies: 0.881 vs. 0.877). It also had a higher predictive accuracy compared with the non-invasive criteria presented in the European Society of Cardiology (ESC) guidelines (average accuracies: 0.877 vs. 0.539). Conclusion: The new non-invasive CFD-based method presented in this work is a promising approach for the accurate diagnosis of CoA, and will likely benefit clinical decision-making.

Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface.

Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world.

Computation of hemodynamics in eccentric coronary stenosis: A morphological parametric study.

BACKGROUND: Flow recirculation occurs in eccentric coronary stenosis, which can lead to adverse outcome. The complex local geodesic patterns of eccentric stenosis are critical factors in determining the flow characteristics in post-stenotic flow. OBJECTIVE: The main objective of this study is to relate the relationship between the detailed morphological parameters in eccentric coronary stenosis and the post-stenotic flow characteristics. METHODS: Several idealized eccentric coronary stenosis models with variable morphological parameters are created to conduct a series of computational fluid dynamics analysis. The impact of four specific lesion morphological parameters, eccentricity index (EI), diameter stenosis (DS), stenosis length (SL) and shape of lesion, are investigated. RESULTS: When EI is small (< 0.33), the length of recirculation zones would increase as EI increase; while when EI is large (> 0.33), it would decreased as EI increase; Larger magnitude of retrograde flow occurs in models with smaller EIs. Both the recirculation zone length and maximum shear rate increase significantly as DS increases. Increase of SL will lead to increase of recirculation zone length. Higher maximum shear rate and larger recirculation zone are observed in models with sharper stenosis shape. CONCLUSIONS: Except DS, the detailed geometry patterns (EI, SL and shape of the stenosis) also have great impact on post-stenotic flow behaviors in eccentric coronary stenosis.