AIMC Topic: Humans

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Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study.

Biomedical engineering online
BACKGROUND: Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuraci...

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations.

BMC bioinformatics
BACKGROUND: The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-le...

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.

eLife
In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by ...

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time.

Journal of neural engineering
. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signa...

Fall Detection With UWB Radars and CNN-LSTM Architecture.

IEEE journal of biomedical and health informatics
Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get wo...

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data.

IEEE journal of biomedical and health informatics
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involve...

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders.

IEEE journal of biomedical and health informatics
We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained ...

A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds.

IEEE journal of biomedical and health informatics
The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise, and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP m...

Extracting Cause of Death From Verbal Autopsy With Deep Learning Interpretable Methods.

IEEE journal of biomedical and health informatics
The international standard to ascertain the cause of death is medical certification. However, in many low and middle-income countries, the majority of deaths occur outside of health facilities. In these cases, Verbal Autopsy (VA), the narrative provi...

A New ECG Denoising Framework Using Generative Adversarial Network.

IEEE/ACM transactions on computational biology and bioinformatics
This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks...