Mendezcochran7030

Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. selleck inhibitor The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.Currently psychiatry is a medical field lacking an automated diagnostic process. The presence of a mental disorder is established by observing its typical symptoms. Eye-movement specifics have already been established as an "endophenotype" for schizophrenia, but an automated diagnostic process of eye-movement analysis is still lacking. This article presents several novel approaches for the automatic detection of a schizophrenic disorder based on a free-view image test using a Rorschach inkblot and an eye tracker. Several features that enabled us to analyse the eye-tracker signal as a whole as well as its specific parts were tested. The variety of features spans global (heat maps, gaze plots), sequences of features (means, variances, and spectra), static (x and y signals as 2D images), dynamic (velocities), and model-based (limiting probabilities and transition matrices) categories. For each set of features, a proper modelling and classification method was designed (convolutional, recurrent, fully connected and combined neural networks; Hidden Markov models). By doing so, it was possible to find the importance of each feature and its physical representation using k-fold cross validation and a paired t-test. The dataset was sampled on 22 people with schizophrenia and 22 healthy individuals. The most successful approach was based on heat maps using all data and convolutional networks, reaching a 78.8% accuracy, which is a 10.5% improvement over the reference method. From all tested methods, there are two in an 85% accuracy range and over fifteen others in a 75% accuracy range at a 10% significance level.

The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system.

A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes.

For a single-meal experiment with preprandial fasting, the trained policy demonstrated fully automated regulation in both the basal and postprandial phases. In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mg/dL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance.

The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake.

The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.

The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.In this contribution, we propose a novel neuromuscular disease detection framework employing weighted visibility graph (WVG) aided analysis of electromyography signals. WVG converts a time series into an undirected graph, while preserving the signal properties. However, conventional WVG is sensitive to noise and has high computational complexity which is problematic for lengthy and noisy time series analysis. To address this issue in this article, we investigate the performance of WVG by varying two important parameters, namely penetrable distance and scale factor, both of which have shown promising results by eliminating the problem of signal adulteration and decreasing the computational complexity, respectively. We also aim to unfold the combined effect of these two aforesaid parameters on the WVG performance to discriminate between myopathy, amyotrophic lateral sclerosis (ALS) and healthy EMG signals. Using graph theory based features we demonstrated that the discriminating capability between the three classes increased significantly with the increase in both penetrable distance and scale factor values. Three binary (healthy vs. myopathy, myopathy vs. ALS and healthy vs. ALS) and one multiclass problems (healthy vs. myopathy vs. ALS) have been addressed in this study and for each problem, we obtained optimum parameter values determined on the basis of F-value computed using one way analysis of variance (ANOVA) test. Using optimal parameter values, we obtained mean accuracy of 98.57%, 98.09% and 99.45%, respectively for three binary and 99.05% for the multi-class classification problem. Additionally, the computational time was reduced by 96% with optimally selected WVG parameters compared to traditional WVG.Panic attacks are an impairing mental health problem that affects 11% of adults every year [1]. Those who suffer from panic attacks often do not seek psychological treatment, citing the inability to receive care during their attacks as a contributing factor. A digital medicine solution which provides an accessible, real-time mobile health (mHealth) biofeedback intervention for panic attacks may address this problem. Critical to this approach are methods for capturing physiological arousal during an attack. Herein, we validate an algorithm for capturing physiological arousal using smartphone video of the fingertip. Results demonstrate that the algorithm is able to estimate heart rates that are highly correlated with ECG-derived values (r > 0.99), effectively reject low-quality data often captured outside of controlled laboratory environments (AUC > 0.90), and resolve the physiological arousal experienced during a panic attack. Moreover, patient reported measures indicate that this measurement modality is feasible during panic attacks, and the act of taking the measurement may stop the attack.