AIMC Topic: Signal Processing, Computer-Assisted

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Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks.

Sensors (Basel, Switzerland)
Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features ...

Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.

Computers in biology and medicine
Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not ha...

Epileptic Seizure Prediction Using Spatiotemporal Feature Fusion on EEG.

International journal of neural systems
Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure prediction has significant value for clinical treatment of epilepsy. Currently, prediction methods using Convolutional Neural Network (CNN) primarily focus ...

Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.

Sensors (Basel, Switzerland)
Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classif...

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

Sensors (Basel, Switzerland)
The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable sy...

Authentication with a one-dimensional CNN model using EEG-based brain-computer interface.

Computer methods in biomechanics and biomedical engineering
Brain-computer interface (BCI) technology uses electroencephalogram (EEG) signals to create a direct interaction between the human body and its surroundings. Motor imagery (MI) classification using EEG signals is an important application that can hel...

A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.

Computers in biology and medicine
In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spect...

Formulation of Common Spatial Patterns for Multi-Task Hyperscanning BCI.

IEEE transactions on bio-medical engineering
This work proposes a new formulation for common spatial patterns (CSP), often used as a powerful feature extraction technique in brain-computer interfacing (BCI) and other neurological studies. In this approach, applied to multiple subjects' data and...

A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.

IEEE transactions on bio-medical engineering
OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment ...

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

IEEE transactions on bio-medical engineering
OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of...