AIMC Topic:
Databases, Factual
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A database for using machine learning and data mining techniques for coronary artery disease diagnosis.
Scientific data
Oct 23, 2019
We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance r...
A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.
Computational and mathematical methods in medicine
Oct 20, 2019
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...
Exploring Deep Physiological Models for Nociceptive Pain Recognition.
Sensors (Basel, Switzerland)
Oct 17, 2019
Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of ...
An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.
Journal of healthcare engineering
Oct 13, 2019
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a chal...
A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 11, 2019
In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people with physical disabilities. With these interfaces, it is very important to decode a user's movement int...
Face Hallucination Using Cascaded Super-Resolution and Identity Priors.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Oct 11, 2019
In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucinatio...
Joint optic disc and cup segmentation using semi-supervised conditional GANs.
Computers in biology and medicine
Oct 10, 2019
Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc...
Identification of postoperative complications using electronic health record data and machine learning.
American journal of surgery
Oct 9, 2019
BACKGROUND: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for ...
Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks.
Computational intelligence and neuroscience
Oct 9, 2019
In recent years, with the explosion of multimedia data from search engines, social media, and e-commerce platforms, there is an urgent need for fast retrieval methods for massive big data. Hashing is widely used in large-scale and high-dimensional da...
A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals.
Computers in biology and medicine
Oct 8, 2019
BACKGROUND: Predicting sex is an important problem in forensic medicine. The femur, patella, mandible and calcaneus bones are frequently used in predicting sex. In our study, we aimed to use the artificial neural network (ANN) technique to predict se...
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