Articles and Recent News
Non-contact Early Warning of Shaking Palsy
Objective: Parkinsonian gait is a defining feature of Shaking Palsy (SP) and it has one of the worse impact on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system based on an S-band perception technique to classify abnormal gait and normal walking.
Reduced Rank Least Squares for Real-Time Short Term Estimation of Mean Arterial Blood Pressure in Septic Patients Receiving Norepinephrine
Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock.
The Conceptual Design of a Novel Workstation for Seizure Prediction using Machine Learning with Potential eHealth Applications
Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most countries, and are not designed specifically for seizure prediction research.
Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images
INTRODUCTION: Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as Intraocular Pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP rely on fundus images of the optic nerves.
Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction from Magnetic Resonance Images
In this article, a novel context dependent fuzzy set associated statistical model based intensity inhomogeneity correction technique for Magnetic Resonance Image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity.
Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes
An early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro proarrhythmia assay and deep learning techniques. We aimed to develop a method to automatically detect irregular beating rhythm of field potentials recorded from human pluripotent stem cells (hPSC) derived cardiomyocytes (hPSC-CM) by multi-electrode array (MEA) system.