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Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study.

JMIR mHealth and uHealth
BACKGROUND: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients wi...

Model of a Support Vector Machine to Assess the Functional Cure for Surgery of Intermittent Exotropia.

Scientific reports
In this paper the optimum timing for the postoperative functional cure of basic intermittent exotropia is explored based on support vector machine (SVM). One hundred and thirty-two patients were recruited in this prospective cross-sectional study wit...

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking.

Journal of neuroengineering and rehabilitation
BACKGROUND: Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure using correlation to data from w...

Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

PloS one
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-impl...

Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.

PloS one
OBJECTIVE: Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictors of future suicidal behavior. The aim of this study was to develop prediction model to identify Kor...

Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis.

IEEE transactions on cybernetics
Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each ta...

Machine learning in resting-state fMRI analysis.

Magnetic resonance imaging
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. W...

Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests.

The Journal of arthroplasty
BACKGROUND: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and ...

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

Computers in biology and medicine
INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among ...