Crouchrytter3806

Meanwhile, the functional connectivity strength also increased obviously in δ and θ band. In sum, we showed positive effect of WM training on psychological performance and explored the neural mechanisms. Our findings may have the implications for enhancing the performance of participants who are prone to cognitive.It is a hot research direction to reveal the working mechanism of brain by measuring the connection characteristics of brain function network. In this paper, to decode pigeon behavior outcomes in goal-directed decision task, an experiment based on plus maze was designed and the nidopallium caudolaterale (NCL) of the pigeon was selected as the target brain region. The local field potential (LFP) signals in the waiting area (WA) and turning area (TA) were recorded when the pigeons performed the goal-directed tasks. Then, the brain functional connection networks of the LFPs were constructed and the extracted features were applied to decode pigeon behavior outcomes. Firstly, continuous wavelet transform (CWT) was used to carried out time-frequency analysis and the task-related frequency band (40-60 Hz) was extracted. Then, weighted sparse representation (WSR) method was used to construct the functional connectivity network and the related network features were selected. Finally, k-nearest neighbor (kNN) algorithm was used to decode behavior outcomes. The results show that the energy difference between TA and WA in 40-60 Hz band is significantly higher than those in other bands. The selected features have good discriminability for the representation of the differences between WA and TA. The decoding results also suggest the classification performance of the different behavior outcomes. These results show the effectiveness of the WSR to construct the function network to decode behavior outcomes.The EEG has showed that contains relevant information about recognition of emotional states. It is important to analyze the EEG signals to understand the emotional states not only from a time series approach but also determining the importance of the generating process of these signals, the location of electrodes and the relationship between the EEG signals. From the EEG signals of each emotional state, a functional connectivity measurement was used to construct adjacency matrices lagged phase synchronization (LPS), averaging adjacency matrices we built a prototype network for each emotion. TAK-779 in vivo Based on these networks, we extracted a set node features seeking to understand their behavior and the relationship between them. We found through the strength and degree, the group of representative electrodes for each emotional state, finding differences from intensity of measurement and the spatial location of these electrodes. In addition, analyzing the cluster coefficient, degree, and strength, we find differences between the networks from the spatial patterns associated with the electrodes with the highest coefficient. This analysis can also gain evidence from the connectivity elements shared between emotional states, allowing to cluster emotions and concluding about the relationship of emotions from EEG perspective.This study had two main objectives (i) to study the effects of volume conduction on different connectivity metrics (Amplitude Envelope Correlation AEC, Phase Lag Index PLI, and Magnitude Squared Coherence MSCOH), comparing the coupling patterns at electrode- and sensor-level; and (ii) to characterize spontaneous EEG activity during different stages of Alzheimer's disease (AD) continuum by means of three complementary network parameters node degree (k), characteristic path length (L), and clustering coefficient (C). Our results revealed that PLI and AEC are weakly influenced by volume conduction compared to MSCOH, but they are not immune to it. Furthermore, network parameters obtained from PLI showed that AD continuum is characterized by an increase in L and C in low frequency bands, suggesting lower integration and higher segregation as the disease progresses. These network changes reflect the abnormalities during AD continuum and are mainly due to neuronal alterations, because PLI is slightly affected by volume conduction effects.The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification approach for the VAR and SS models, based on Least Absolute Shrinkage and Selection Operator (LASSO), that has the advantages of maintaining good accuracy even when few data samples are available and yielding as output a sparse matrix of estimated information transfer. The performances of LASSO identification were first tested and compared to those of OLS by a simulation study and then validated on real electroencephalographic (EEG) signals recorded during a motor imagery task. Both studies indicated that LASSO, under conditions of data paucity, provides better performances in terms of network structure. Given the general nature of the model, this work opens the way to the use of LASSO regression for the computation of several measures of information dynamics currently in use in computational neuroscience.The potential of using the information of uterine contractions (UCs) derived from electrohysterogram (EHG) has been recognized in early detection of preterm delivery. A better understanding of the conduction property of EHG is clinically useful for developing advanced methods to achieve a reliable prediction of preterm delivery. In this paper, a method to analyze the destination of EHG propagation has been proposed via the estimation of directed information (DI) between each pair of neighboring channels with a novel propagation terminal zone (PTZ) identification algorithm. The proposed method was applied to experimental data from the Icelandic 16-electrode EHG database. The results demonstrated that for more than 81.8% participants, the PTZ was identified along the medial axis of uterus, among which more than half have their PTZ determined in the center between the uterine fundus and public symphysis, which indicated a great probability of propagation of EHG signals towards the center of uterus plane.Clinical relevance- This study makes a fundamental contribution for predicting preterm delivery, which can provide improvement in obstetric care towards pregnancy monitoring.Cardiography enables diagnostic and preventive care in hospitals and outpatient scenarios. However, most heart monitors do not distinguish the phases of the cardiac cycle. The transition between phases is indicated by the primary heart sounds.

Automatically identify the vibrations corresponding to both heart sounds.

Cardiac activity was monitored for 15 subjects while at rest, during exertion, and while performing static breath holds. The subjects consisted of 6 males and 9 females between the ages of 18-39 years with no known cardiorespiratory ailments. Motion corresponding to the heart sounds was identified using vibrational cardiography (VCG). The waveforms were processed to obtain quantities associated with their linear jerk and rotational kinetic energy.

The ability to identity the first vibration was evaluated using the heart rate as a figure of merit. Its correlation with electrocardiography (ECG) measurements produced a r

coefficient of 0.9887. The second vibration was compared with impedance cardiography (ICG) based on its delay from the ECG R-peak, and the fraction of the beat duration occupied by left ventricular ejection time. The comparisons produced r

values of 0.251 and 0.2797, respectively.

The vibrations corresponding to both primary heart sounds have the potential to be analyzed using VCG.

This study provides evidence of the feasibility of using VCG in identifying mechanical cardiovascular function. It facilitates non-invasive cardiac health monitoring in daily life.

This study provides evidence of the feasibility of using VCG in identifying mechanical cardiovascular function. It facilitates non-invasive cardiac health monitoring in daily life.The objective of quantitative ultrasound (QUS) is to characterize tissue microstructure by parametrizing backscattered radiofrequency (RF) signals from clinical ultrasound scanners. Herein, we develop a novel technique based on dynamic programming (DP) to simultaneously estimate the acoustic attenuation, the effective scatterer size (ESS), and the acoustic concentration (AC) from ultrasound backscattered power spectra. This is achieved through two different approaches (1) using a Gaussian form factor (GFF) and (2) using a general form factor (gFF) that is more flexible than the Gaussian form factor but involves estimating more parameters. Both DP methods are compared to an adaptation of a previously proposed least-squares (LSQ) method. Simulation results show that in the GFF approach, the variance of DP is on average 88%, 75% and 32% lower than that of LSQ for the three estimated QUS parameters. The gFF approach also yields similar improvements.In this paper, a novel pre-treatment technique Hilbert Huang Transformation with filtering (HHTF) that is coupling of the Hilbert Huang Transformation and the digital filtering is proposed for the measurement of glucose from near infrared spectroscopy. HHTF comprises of the Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis. In Hilbert spectral analysis, Butterworth filtering was used to eliminate the noise present in the Intrinsic Mode Functions (IMFs). The traditional Partial Least squares Regression (PLSR) has been used as the regression method. The proposed HHTF with the PLSR method has been assessed to determine the concentration of glucose from near infrared spectra of two distinct compositions that are prepared by mixing triacetin, urea and glucose in a phosphate buffer solution (PBS) and another composition of glucose and human serum albumin in a PBS. The efficiency of the proposed method has been compared with the standard normal variate and the 1st derivative preprocessing methods and is shown to outperform both.Prolonged measurement of total body volume variations (deltaVb) with whole-body, flow-based plethysmography (WBP) results in a drift of the signal due to changes in temperature and humidity inside the plethysmograph and to numerical integration of the flow to obtain deltaVb. This drift has been previously corrected with the application of a wavelet- based filter using visual inspection of the signal to select the optimal filter level (Uva et al. Front. Physiol. 6411, 2016), thus introducing potential operator bias. To exclude the latter we compared this approach with a newly developed automatic method based on (1) correction for actual changes in temperature and humidity inside the plethysmograph (algorithm TH) and (2) automatic selection of the wavelet filter level based on comparison between deltaVb and intra-thoracic and abdominal pressure variations measured simultaneously (algorithm WAV). The Pearson's correlation coefficient between deltaVb and the changes in volume of the chest wall (deltaVcw) simultaneously obtained by optoelectronic plethysmography (OEP) was calculated after correction of deltaVb with TH and WAV applied separately, TH and WAV applied consecutively (TH+WAV), manual selection of a wavelet filter based on visual inspection (MAN) or no correction (CTRL).