Consciousness-ECG Transformer for Conscious State Estimation System with Real-Time Monitoring
Young-Seok Kweon, Gi-Hwan Shin, Ji-Yong Kim, Bokyeong Ryu, Seong-Whan Lee

TL;DR
This paper introduces a transformer-based system that uses ECG signals for real-time conscious state estimation, offering a non-invasive alternative to EEG with high accuracy in sleep and anesthesia monitoring.
Contribution
The study presents a novel ECG transformer model with decoupled query attention for effective conscious state detection, validated on clinical datasets with superior performance.
Findings
Achieved 87.7% accuracy in sleep staging
Achieved 88.0% accuracy in anesthesia monitoring
Highest AUC scores of 0.786 and 0.895 in respective tasks
Abstract
Conscious state estimation is important in various medical settings, including sleep staging and anesthesia management, to ensure patient safety and optimize health outcomes. Traditional methods predominantly utilize electroencephalography (EEG), which faces challenges such as high sensitivity to noise and the requirement for controlled environments. In this study, we propose the consciousness-ECG transformer that leverages electrocardiography (ECG) signals for non-invasive and reliable conscious state estimation. Our approach employs a transformer with decoupled query attention to effectively capture heart rate variability features that distinguish between conscious and unconscious states. We implemented the conscious state estimation system with real-time monitoring and validated our system on datasets involving sleep staging and anesthesia level monitoring during surgeries.…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Non-Invasive Vital Sign Monitoring · ECG Monitoring and Analysis
