AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

Clear Filters Showing 1001 to 1010 of 1879 articles

A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram.

Journal of neuroscience methods
BACKGROUND: Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data...

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line.

Bioinspiration & biomimetics
The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive artificial lateral line (ALL) comprising eight all-optical flow sensors,...

A Feasible Feature Extraction Method for Atrial Fibrillation Detection From BCG.

IEEE journal of biomedical and health informatics
Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here,...

Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals.

Computers in biology and medicine
There have been different efforts to predict epileptic seizures and most of them are based on the analysis of electroencephalography (EEG) signals; however, recent publications have suggested that functional Near-Infrared Spectroscopy (fNIRS), a rela...

BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity.

IEEE transactions on biomedical circuits and systems
Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. N...

Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals.

BioMed research international
Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) f...

A practical guide to intelligent image-activated cell sorting.

Nature protocols
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell s...

Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.

Sensors (Basel, Switzerland)
A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery...

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation.

Medical & biological engineering & computing
Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using electroencephalographic data, those spurious interactions are mostly caused by the volume conduction ...

Learning-based classification of valence emotion from electroencephalography.

The International journal of neuroscience
The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist such systems, electroencephalography (EEG) based measures play an important role. In this study, th...