AIMC Topic: Signal Processing, Computer-Assisted

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A subject transfer neural network fuses Generator and Euclidean alignment for EEG-based motor imagery classification.

Journal of neuroscience methods
BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the signific...

Enhanced Graph Attention Network by Integrating Transformer for Epileptic EEG Identification.

International journal of neural systems
Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing t...

Heart Rate and Body Temperature Relationship in Children Admitted to PICU: A Machine Learning Approach.

IEEE transactions on bio-medical engineering
UNLABELLED: Vital signs are crucial clinical measures, with body temperature (BT) and heart rate (HR) being particularly significant. While their association has been studied in adults and children, research in Pediatric Intensive Care Unit (PICU) se...

Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.

IEEE transactions on bio-medical engineering
OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communica...

An Advanced Self-Similarity Measure: Average of Level-Pairwise Hurst Exponent Estimates (ALPHEE).

IEEE transactions on bio-medical engineering
Many natural processes are characterized by complex patterns of self-similarity, where repetitive structures occur across different resolutions. The Hurst exponent is a key parameter used to quantify this self-similarity. While wavelet-based techniqu...

A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.

Medical engineering & physics
Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats...

Novel fusion-based time-frequency analysis for early prediction of sudden cardiac death from electrocardiogram signals.

Medical engineering & physics
Sudden cardiac death (SCD) is one of the leading causes of global mortality, often occurring without warning and driven by complex cardiac dynamics. Despite significant advances in cardiovascular diagnostics, accurately predicting SCD at an early sta...

Ultra-low-power System-on-Chip for automated screening of central apnea and hypopnea via chin electromyography.

Computers in biology and medicine
Central Apnea (CA) and Central Hypopnea (CH) are sleep disorders arising from the brain's inability to signal respiratory muscles, potentially leading to severe complications such as heart failure. This study presents a novel system for automating CA...

Advancing emotion recognition with Virtual Reality: A multimodal approach using physiological signals and machine learning.

Computers in biology and medicine
INTRODUCTION: Emotion recognition systems have traditionally relied on basic visual elicitation. Virtual reality (VR) offers an immersive alternative that better resembles real-world emotional experiences.

Speech signals-based Parkinson's disease diagnosis using hybrid autoencoder-LSTM models.

Computers in biology and medicine
Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages ha...