News-Medical.Net

AI model identifies where snoring starts in the airway

A new study developed a snore-source classification model that uses STFT spectrograms, pretrained CNN features, and an L2-regularized SVM to identify where snoring originates in the upper airway.
IEEE

Enhancing Automated Seizure Detection via Self-Calibrating Spatial-Temporal EEG Features with SC-LSTM

Abstract: Epilepsy, a highly individualized neurological disorder, affects millions globally. Electroencephalography (EEG) remains the cornerstone for seizure diagnosis, yet manual interpretation is ...
IEEE

Multi-Entropy Feature Extraction With LSTM Networks for Acoustic Emission-Based Railway Crack Localization

Abstract: Railway infrastructure security is contingent upon the prompt identification of structural anomalies within steel tracks. This research establishes a framework that merges acoustic emission ...
Frontiers

HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition

EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and ...
Scientific Research Publishing

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting ()

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit ...
Frontiers

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification ...