AIMC Topic: Signal Processing, Computer-Assisted
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EMG-based lumbosacral joint compression force prediction using a support vector machine.
Medical engineering & physics
Sep 17, 2019
Electromyography-assisted optimization (EMGAO) approach is widely used to predict lumbar joint loads under various dynamic and static conditions. However, such approach uses numerous anthropometric, kinematic, kinetic, and electromyographic data in t...
Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net.
IEEE journal of biomedical and health informatics
Sep 16, 2019
In this paper, deep belief net (DBN) was applied into the field of wearable-sensor based Chinese sign language (CSL) recognition. Eight subjects were involved in the study, and all of the subjects finished a five-day experiment performing CSL on a ta...
Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.
PLoS computational biology
Sep 16, 2019
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear ...
A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns.
IEEE journal of biomedical and health informatics
Sep 13, 2019
OBJECTIVE: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently...
Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea.
Scientific reports
Sep 13, 2019
The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for t...
Decoding Brain States From fMRI Signals by Using Unsupervised Domain Adaptation.
IEEE journal of biomedical and health informatics
Sep 11, 2019
With the development of deep learning in medical image analysis, decoding brain states from functional magnetic resonance imaging (fMRI) signals has made significant progress. Previous studies often utilized deep neural networks to automatically clas...
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.
Computational intelligence and neuroscience
Sep 9, 2019
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. H...
Nonconvulsive epileptic seizure monitoring with incremental learning.
Computers in biology and medicine
Sep 6, 2019
Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidity and mortality in critically ill patients. In a previous work, we introduced a method which accura...
World's fastest brain-computer interface: Combining EEG2Code with deep learning.
PloS one
Sep 6, 2019
We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus...
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.
Circulation. Cardiovascular quality and outcomes
Sep 5, 2019
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...
Humans
Prognosis
Reproducibility of Results
Cardiovascular Diseases
Diagnosis, Computer-Assisted
Neural Networks, Computer
Machine Learning
Databases, Factual
Markov Chains
Predictive Value of Tests
Pattern Recognition, Automated
Signal Processing, Computer-Assisted
Time Factors
Workflow
Electrocardiography
Heart Rate
Action Potentials
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