AIMC Topic: Signal Processing, Computer-Assisted

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Multivariate Glucose Forecasting Using Deep Multihead Attention Layers Inside Neural Basis Expansion Networks.

IEEE journal of biomedical and health informatics May 6, 2025
Glucose forecasting is a crucial feature in a closed-loop diabetes management system relying on minimally invasive continuous glucose monitoring (CGM) sensors. Forecasting is required to prevent hyperglycaemia or hypoglycaemia due to delayed or incor...
Blood Glucose Self-Monitoring Deep Learning Blood Glucose Humans Signal Processing, Computer-Assisted Neural Networks, Computer
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Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition.

IEEE journal of biomedical and health informatics May 6, 2025
Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to ...
Humans Algorithms Female Unsupervised Machine Learning Young Adult Brain-Computer Interfaces Electroencephalography Imagination Signal Processing, Computer-Assisted Adult Male
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A Distributed Neural Network Architecture for Dynamic Sensor Selection With Application to Bandwidth-Constrained Body-Sensor Networks.

IEEE journal of biomedical and health informatics May 6, 2025
We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. This dynamic selection is j...
Signal Processing, Computer-Assisted Wireless Technology Neural Networks, Computer Algorithms Humans Electroencephalography
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MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics May 6, 2025
Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global tempora...
Databases, Factual Female Young Adult Adult Male Humans Neural Networks, Computer Algorithms Polysomnography Sleep Stages Electroencephalography Signal Processing, Computer-Assisted
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CLEAR-Shock: Contrastive LEARning for Shock.

IEEE journal of biomedical and health informatics May 6, 2025
Shock is a life-threatening condition characterized by generalized circulatory failure, which can have devastating consequences if not promptly treated. Thus, early prediction and continuous monitoring of physiological signs are essential for timely ...
Machine Learning Prognosis Humans Monitoring, Physiologic Signal Processing, Computer-Assisted Shock
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FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.

IEEE journal of biomedical and health informatics May 6, 2025
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...
Polysomnography Sleep Stages Adult Deep Learning Signal Processing, Computer-Assisted Neural Networks, Computer Databases, Factual Humans
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TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

IEEE journal of biomedical and health informatics May 6, 2025
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...
Electrocardiography Signal Processing, Computer-Assisted Fetus Female Abdomen Fetal Monitoring Generative Adversarial Networks Humans Neural Networks, Computer Pregnancy
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A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning.

IEEE journal of biomedical and health informatics May 6, 2025
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...
Machine Learning Wearable Electronic Devices Electrocardiography Humans Signal Processing, Computer-Assisted Intelligent Systems
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Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning.

IEEE journal of biomedical and health informatics May 6, 2025
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,...
Electrooculography Galvanic Skin Response Electroencephalography Signal Processing, Computer-Assisted Young Adult Eye Movements Humans Adult Machine Learning Cognition Male Female Electrocardiography
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Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals.

Neuroscience May 5, 2025
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...
Humans Epilepsy Signal Processing, Computer-Assisted Entropy Neural Networks, Computer Deep Learning Electroencephalography
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