AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

Clear Filters Showing 1081 to 1090 of 1882 articles

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.

Sensors (Basel, Switzerland)
The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutio...

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.

Computational intelligence and neuroscience
Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is ...

In situ tissue classification during laser ablation using acoustic signals.

Journal of biophotonics
We suggest a novel method to classify the type of tissue that is being ablated, using the recorded acoustic sound waves during pulsed ultraviolet laser ablation. The motivation of the current research is tissue classification during vascular interven...

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification.

IEEE journal of biomedical and health informatics
Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. ...

A novel IRBF-RVM model for diagnosis of atrial fibrillation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learn...

FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals.

Sensors (Basel, Switzerland)
Recently, pervasive sensing technologies have been widely applied to comprehensive patient monitoring in order to improve clinical treatment. Various types of biomedical signals collected by different sensing channels provide different aspects of pat...

Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification.

Journal of neuroscience methods
BACKGROUND: Motor imagery classification, an important branch of brain-computer interface (BCI), recognizes the intention of subjects to control external auxiliary equipment. Therefore, EEG-based motor imagery classification has received increasing a...

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

Computers in biology and medicine
The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurren...

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

PloS one
Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (V...