AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

Clear Filters Showing 621 to 630 of 1839 articles

Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning.

Sensors (Basel, Switzerland)
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly p...

Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...

Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.

Scientific reports
Neuroscientific analyses balance between capturing the brain's complexity and expressing that complexity in meaningful and understandable ways. Here we present a novel approach that fully characterizes neural network activity and does so by uniquely ...

Dual-Modal Information Bottleneck Network for Seizure Detection.

International journal of neural systems
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimens...

Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review.

IEEE reviews in biomedical engineering
Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data. Graph-based approaches have shed light on decoding bio...

A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation.

Sensors (Basel, Switzerland)
With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive. It has become extremely important to extract useful signals by decomposing various mixed signals through blind so...

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Scientific reports
Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it ...

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals.

Sensors (Basel, Switzerland)
Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with...

Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices.

Journal of voice : official journal of the Voice Foundation
OBJECTIVE: To analyze the performance of 10 different machine learning (ML) classifiers for discrimination between dysphonic and non-dysphonic voices, using a variance threshold as a method for the selection and reduction of acoustic measurements use...

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review.

Sensors (Basel, Switzerland)
Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potenti...