Newly identified Phonocardiography frequency bands for psychological stress detection with Deep Wavelet Scattering Network.

The timely psychological stress detection can improve the quality of human life by preventing stress-induced behavioral and pathological consequences. This paper presents a novel framework that eliminates the need of Electrocardiography (ECG) signals-based referencing of Phonocardiography (PCG) signals for psychological stress detection. This stand-alone PCG-based methodology uses wavelet scattering approach on the data acquired from twenty-eight healthy adult male and female subjects to detect psychological stress. The acquired PCG signals are asynchronously segmented for the analysis using wavelet scattering transform. After the noise bands removal, the optimized segmentation length (L), scattering network parameters namely-invariance scale (J) and quality factor (Q) are utilized for computation of scattering features. These scattering coefficients generated are fed to K-nearest neighbor (KNN) and Extreme Gradient Boosting (XGBoost) classifier and the ten-fold cross validation-based performance metrics obtained are-accuracy 94.30 %, sensitivity 97.96 %, specificity 88.01 % and area under the curve (AUC) 0.9298 using XGBoost classifier for detecting psychological stress. Most importantly, the framework also identified two frequency bands in PCG signals with high discriminatory power for psychological stress detection as 270-290 Hz and 380-390 Hz. The elimination of multi-modal data acquisition and analysis makes this approach cost-efficient and reduces computational complexity.

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

Exploring parenthood in first episode of psychosis: the potential role of the offspring in the outcome of women.

PURPOSE: The study aimed to explore the role of parenthood at first episode of psychosis (FEP) on recovery, with a focus on potential sex differences. METHODS: Sociodemographic, clinical, and neurocognitive information was considered on 610 FEP patients form the PAFIP cohort (Spain). Baseline and three-year follow-up comparisons were carried out. Chi-square tests and ANCOVA analysis were performed controlling for the effect of age and years of education. RESULTS: Men comprised 57.54% of the sample, with only 5.41% having offspring when compared to 36.29% of women. Parenthood was related to shorter duration of untreated illness (DUI) in women with children (12.08 months mothers vs. 27.61 months no mothers), showing mothers better premorbid adjustment as well. Childless men presented the worst premorbid adjustment and the highest cannabis and tobacco consumption rates. Mothers presented better global cognitive function, particularly in attention, motor dexterity and executive function at three-year follow-up. CONCLUSIONS: Diminished parental rates among FEP men could be suggested as a consequence of a younger age of illness onset. Sex roles in caregiving may explain the potential role of parenthood on premorbid phase, with a better and heathier profile, and a more favorable long-term outcome in women. These characteristics may be relevant when adjusting treatment specific needs in men and women with and without offspring.

Primary color decoding using deep learning on source reconstructed EEG signal responses.

The brain's response to visual stimuli of different colors might be used in a brain-computer interface (BCI) paradigm, for letting a user control their surroundings by looking at specific colors. Allowing the user to control certain elements in its environment, such as lighting and doors, by looking at corresponding signs of different colors could serve as an intuitive interface. This paper presents work on the development of an intra-subject classifier for red, green, and blue (RGB) visual evoked potentials (VEPs) in recordings performed with an electroencephalogram (EEG). Three deep neural networks (DNNs), proposed in earlier papers, were employed and tested for data in source- and electrode space. All the tests performed in electrode space yielded better results than those in source space. The best classifier yielded an accuracy of 77% averaged over all subjects, with the best subject having an accuracy of 96%.Clinical relevance- This paper demonstrates that deep learning can be used to classify between red, green and blue visual evoked potentials in EEG recordings with an average accuracy of 77%.

A Proxy Approach to Family Involvement and Neurocognitive Function in First Episode of Non-Affective Psychosis: Sex-Related Differences.

Schizophrenia spectrum disorders (SSD) often show cognitive deficits (CD) impacting daily life. Family support has been shown to be protective against CD, yet the relationship between these in psychotic patients remains complex and not fully understood. This study investigated the association between a subdomain of family support, namely, family involvement (estimated through a proxy measure), cognitive functioning, and sex in first-episode psychosis (FEP) patients. The sample included 308 patients enrolled in the Program for Early Phases of Psychosis (PAFIP), divided into 4 groups based on their estimated family involvement (eFI) level and sex, and compared on various variables. Women presented lower rates of eFI than men (37.1% and 48.8%). Higher eFI was associated with better cognitive functioning, particularly in verbal memory. This association was stronger in women. The findings suggest that eFI may be an important factor in FEP patients' cognitive functioning. This highlights the importance of including families in treatment plans for psychotic patients to prevent CD. Further research is needed to better understand the complex interplay between family support, sex, and cognitive functioning in psychotic patients and develop effective interventions that target these factors.

Multi-Objective Optimisation of a Novel Bypass Graft with a Spiral Ridge.

The low long-term patency of bypass grafts is a major concern for cardiovascular treatments. Unfavourable haemodynamic conditions in the proximity of distal anastomosis are closely related to thrombus creation and lumen lesions. Modern graft designs address this unfavourable haemodynamic environment with the introduction of a helical component in the flow field, either by means of out-of-plane helicity graft geometry or a spiral ridge. While the latter has been found to lack in performance when compared to the out-of-plane helicity designs, recent findings support the idea that the existing spiral ridge grafts can be further improved in performance through optimising relevant design parameters. In the current study, robust multi-objective optimisation techniques are implemented, covering a wide range of possible designs coupled with proven and well validated computational fluid dynamics (CFD) algorithms. It is shown that the final set of suggested design parameters could significantly improve haemodynamic performance and therefore could be used to enhance the design of spiral ridge bypass grafts.

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization.

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition.

Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful a priori time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as a priori information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.

Optimisation of a Novel Spiral-Inducing Bypass Graft Using Computational Fluid Dynamics.

Graft failure is currently a major concern for medical practitioners in treating Peripheral Vascular Disease (PVD) and Coronary Artery Disease (CAD). It is now widely accepted that unfavourable haemodynamic conditions play an essential role in the formation and development of intimal hyperplasia, which is the main cause of graft failure. This paper uses Computational Fluid Dynamics (CFD) to conduct a parametric study to enhance the design and performance of a novel prosthetic graft, which utilises internal ridge(s) to induce spiral flow. This design is primarily based on the identification of the blood flow as spiral in the whole arterial system and is believed to improve the graft longevity and patency rates at distal graft anastomoses. Four different design parameters were assessed in this work and the trailing edge orientation of the ridge was identified as the most important parameter to induce physiological swirling flow, while the height of the ridge also significantly contributed to the enhanced performance of this type of graft. Building on these conclusions, an enhanced configuration of spiral graft is proposed and compared against conventional and spiral grafts to reaffirm its potential benefits.

Numerical Assessment of Novel Helical/Spiral Grafts with Improved Hemodynamics for Distal Graft Anastomoses.

In the present work, numerical simulations were conducted for a typical end-to-side distal graft anastomosis to assess the effects of inducing secondary flow, which is believed to remove unfavourable flow environment. Simulations were carried out for four models, generated based on two main features of 'out-of-plane helicity' and 'spiral ridge' in the grafts as well as their combination. Following a qualitative comparison against in vitro data, various mean flow and hemodynamic parameters were compared and the results showed that helicity is significantly more effective in inducing swirling flow in comparison to a spiral ridge, while their combination could be even more effective. In addition, the induced swirling flow was generally found to be increasing the wall shear stress and reducing the flow stagnation and particle residence time within the anastomotic region and the host artery, which may be beneficial to the graft longevity and patency rates. Finally, a parametric study on the spiral ridge geometrical features was conducted, which showed that the ridge height and the number of spiral ridges have significant effects on inducing swirling flow, and revealed the potential of improving the efficiency of such designs.