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Effects of medium- and long-distance running on cardiac damage markers in amateur runners: a systematic review, meta-analysis, and metaregression.

Journal of sport and health science
BACKGROUND: To finish an endurance race, athletes perform a vigorous effort that induces the release of cardiac damage markers. There are several factors that can affect the total number of these markers, so the aim of this review was to analyze the ...

Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the en...

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data.

Scientific reports
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accel...

Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people.

Journal of neuroengineering and rehabilitation
BACKGROUND: Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) p...

Predicting the serum digoxin concentrations of infants in the neonatal intensive care unit through an artificial neural network.

BMC pediatrics
BACKGROUND: Given its narrow therapeutic range, digoxin's pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to suppor...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Physiological measurement
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 ...

Automated detection & classification of knee arthroplasty using deep learning.

The Knee
BACKGROUND: Preoperative identification of knee arthroplasty is important for planning revision surgery. However, up to 10% of implants are not identified prior to surgery. The purposes of this study were to develop and test the performance of a deep...

A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, ...

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

Medical physics
PURPOSE: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the ac...