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Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks
AbstractIt is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse strok...
Source: European Journal of Applied Physiology - August 17, 2023 Category: Physiology Source Type: research

Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke
Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.
Source: Frontiers in Physiology - March 22, 2023 Category: Physiology Source Type: research

Multi-modal fusion model for predicting adverse cardiovascular outcome post percutaneous coronary intervention
Conclusion. To the best of our knowledge, this is the first study that developed a deep learning model with joint fusion architecture for the prediction of post-PCI prognosis and outperformed machine learning models developed using traditional single-source features (clinical variables or E CG features). Adding ECG data with clinical variables did not improve prediction of all-cause mortality as may be expected, but the improved performance of related cardiac outcomes shows that the fusion of ECG generates additional value.
Source: Physiological Measurement - December 22, 2022 Category: Physiology Authors: Amartya Bhattacharya, Sudarsan Sadasivuni, Chieh-Ju Chao, Pradyumna Agasthi, Chadi Ayoub, David R Holmes, Reza Arsanjani, Arindam Sanyal and Imon Banerjee Source Type: research

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke
Conclusion: This work compares the validity of methods classifying stroke survivors’ real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.
Source: Frontiers in Physiology - September 28, 2022 Category: Physiology Source Type: research

Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke
Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our stud...
Source: Frontiers in Physiology - May 3, 2022 Category: Physiology Source Type: research

Scene text removal via cascaded text stroke detection and erasing
AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results dem...
Source: European Journal of Applied Physiology - December 16, 2021 Category: Physiology Source Type: research

Central Hypovolemia Detection During Environmental Stress —A Role for Artificial Intelligence?
The first step to exercise is preceded by the required assumption of the upright body position, which itself involves physical activity. The gravitational displacement of blood from the chest to the lower parts of the body elicits a fall in central blood volume (CBV), which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. The reduction in CBV and stroke volume (SV) in response to postural stress, post-exercise, or to blood loss results in reduced left ventricular filling, which may manifest as orthostatic intolerance. When termination of exercise removes the leg muscle pump fun...
Source: Frontiers in Physiology - December 15, 2021 Category: Physiology Source Type: research

Scene text removal via cascaded text stroke detection and erasing
AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results dem...
Source: European Journal of Applied Physiology - December 6, 2021 Category: Physiology Source Type: research

Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network
Conclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.
Source: Frontiers in Physiology - August 25, 2021 Category: Physiology Source Type: research

Fast Hand Movements Unveil Multifractal Roots of Adaptation in the Visuomotor Cognitive System
Beyond apparent simplicity, visuomotor dexterity actually requires the coordination of multiple interactions across a complex system that links the brain, the body and the environment. Recent research suggests that a better understanding of how perceptive, cognitive and motor activities cohere to form executive control could be gained from multifractal formalisms applied to movement behavior. Rather than a central executive “talking” to encapsuled components, the multifractal intuition suggests that eye-hand coordination arises from multiplicative cascade dynamics across temporal scales of activity within the whole sys...
Source: Frontiers in Physiology - July 20, 2021 Category: Physiology Source Type: research

Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events
ConclusionVisualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.
Source: Frontiers in Physiology - February 18, 2021 Category: Physiology Source Type: research

Prediction of Cardiac Mechanical Performance From Electrical Features During Ventricular Tachyarrhythmia Simulation Using Machine Learning Algorithms
In this study, we predicted cardiac mechanical performance from features of electrical instability during ventricular tachyarrhythmia simulation using machine learning algorithms, including support vector regression (SVR) and artificial neural network (ANN) models. We performed an electromechanical tachyarrhythmia simulation and extracted 12 electrical instability features and two mechanical properties, including stroke volume and the amplitude of myocardial tension (ampTens). We compared predictive performance according to kernel types of the SVR model and the number of hidden layers of the ANN model. In the SVR model, th...
Source: Frontiers in Physiology - November 24, 2020 Category: Physiology Source Type: research

A single, clinically relevant dose of the GABAB agonist baclofen impairs visuomotor learning.
This article is protected by copyright. All rights reserved. PMID: 33085094 [PubMed - as supplied by publisher]
Source: The Journal of Physiology - October 21, 2020 Category: Physiology Authors: Johnstone A, Grigoras I, Petitet P, Capitão LP, Stagg CJ Tags: J Physiol Source Type: research

Glucose transporters in brain in health and disease.
Abstract Energy demand of neurons in brain that is covered by glucose supply from the blood is ensured by glucose transporters in capillaries and brain cells. In brain, the facilitative diffusion glucose transporters GLUT1-6 and GLUT8, and the Na+-D-glucose cotransporters SGLT1 are expressed. The glucose transporters mediate uptake of D-glucose across the blood-brain barrier and delivery of D-glucose to astrocytes and neurons. They are critically involved in regulatory adaptations to varying energy demands in response to differing neuronal activities and glucose supply. In this review, a comprehensive overview abo...
Source: Pflugers Archiv : European Journal of Physiology - August 12, 2020 Category: Physiology Authors: Koepsell H Tags: Pflugers Arch Source Type: research

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation
Objective : Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as continuous long-term monitoring of heart arrhythmias, fitness, and sleep tracking, and hydration monitoring. One major issue that can hinder PPG-based applications is movement artifacts, which can lead to false interpretations. In many implementations, noisy PPG signals are discarded. Misinterpreted or discarded PPG signals pose a problem in applications where the goal i...
Source: Physiological Measurement - December 26, 2019 Category: Physiology Authors: Tania Pereira, Cheng Ding, Kais Gadhoumi, Nate Tran, Rene A Colorado, Karl Meisel and Xiao Hu Source Type: research

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