40 citations · 133 across the 43 of their papers we have counts for
6 papers · 1 filter
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah +3
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morph…
HRnV-Calc: A software package for heart rate n-variability and heart rate variability analysis
Chenglin Niu, Dagang Guo, Marcus Eng Hock Ong +6
Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments…
The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
Jorge Oliveira, Francesco Renna, Paulo Dias Costa +12
Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can su…
Privacy-Preserving Eye-tracking Using Deep Learning
Salman Seyedi, Zifan Jiang, Allan Levey +1
The expanding usage of complex machine learning methods like deep learning has led to an explosion in human activity recognition, particularly applied to health. In particular, as…
Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure
Ayse S. Cakmak, Samuel Densen, Gabriel Najarro +5
Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Early detection of HF symptoms and pro-active…
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Salman Seyedi, Li Xiong, Shamim Nemati +1
The increasing complexity of algorithms for analyzing medical data, including de-identification tasks, raises the possibility that complex algorithms are learning not just the gene…