Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …

The rapid advancement in healthcare data collection technologies and the importance of
using multimodal data for accurate diagnosis leads to a surge in multimodal data …

Electronic health records (EHRs) contain diverse patient information, including medical
notes, clinical events, and laboratory test results. Integrating this multimodal data can …

Accurate monitoring of the depth of anesthesia (DoA) is essential for ensuring patient safety
and effective anesthesia management. Existing methods, such as the Bispectral Index (BIS) …

Administration of anesthesia is essential in surgical procedures, ensuring patient
unconsciousness and safety. Traditional Depth of Anesthesia (DoA) assessment methods …

Objective: Recent quantification research on Parkinson's disease (PD) integrates wearable
technology with machine learning methods, indicating a strong potential for practical …

Intelligent recognition of multi-scale marine targets remains pivotal in studying marine
resources and transportation. Multi-scale marine target recognition faces challenges such as …

Electrocardiogram (ECG) classification is pivotal in diagnosing and monitoring
cardiovascular diseases (CVDs). However, existing methods predominantly rely on hand …

Objective. Electroencephalogram (EEG) recordings often contain large segments with
missing signals due to poor electrode contact or other artifact contamination. Recovering …

To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in
this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi …