AIMC Topic: Signal Processing, Computer-Assisted

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FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.

IEEE journal of biomedical and health informatics
In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction with computational complexity. To address this issue, this study introduces FlexibleSleepNet, a lightw...

TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

IEEE journal of biomedical and health informatics
Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged by maternal ECG (MECG) and background noise in abdominal ECG (AECG), it is challenging to exactly re...

A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning.

IEEE journal of biomedical and health informatics
This paper conducts an extensive review of flexible cardiac sensing devices designed for electrocardiogram (ECG) acquisitions, with emphasis on their application in cardiac health monitoring. This study focuses on characteristics crucial to these dev...

Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning.

IEEE journal of biomedical and health informatics
Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification,...

Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals.

Neuroscience
Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel approach for the automatic identification of epilepsy in EEG signals by incorporating advanced entropy-ba...

A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Sensors (Basel, Switzerland)
Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite t...

Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction.

Artificial intelligence in medicine
This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal signal integration framework encompassing Electrooculography (EOG) and two-channel electroencephalography (EEG) data. We explore the utility of vario...

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.

IEEE transactions on neural networks and learning systems
The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manua...

Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

Computers in biology and medicine
The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (ele...

TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain entropy features.

Journal of neuroscience methods
BACKGROUND: Taste perception is the process by which the gustatory system detects and interprets chemical stimuli from food and beverages, involving activation of taste receptors on the tongue. Analyzing taste perception is essential for understandin...