AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

Clear Filters Showing 781 to 790 of 1839 articles

Interpreting deep learning models for epileptic seizure detection on EEG signals.

Artificial intelligence in medicine
While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-ligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability ...

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.

Journal of biological physics
The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet ...

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution.

Journal of neural engineering
The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the cha...

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Journal of healthcare engineering
Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep ne...

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Computational and mathematical methods in medicine
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation...

Machine learning for detection of interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis o...

Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples.

Neural networks : the official journal of the International Neural Network Society
This paper deals with the development of a novel deep learning framework to achieve highly accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional auto-encoder. Unlike most existing methods, in which the input data i...

Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence.

BioMed research international
The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel diagnostics that may significantly enhance the cardiology care pathway. Cardiac phase space analysis is a noninvasive diagnostic platform that combin...

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...