Consciousness-ECG Transformer for Conscious State Estimation System with Real-Time Monitoring

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. Experimental results demonstrate that our model outperforms baseline models, achieving accuracies of 0.877 on sleep staging and 0.880 on anesthesia level monitoring. Moreover, our model achieves the highest area under curve values of 0.786 and 0.895 on sleep staging and anesthesia level monitoring, respectively. The proposed system offers a practical and robust alternative to EEG-based methods, particularly suited for dynamic clinical environments. Our results highlight the potential of ECG-based consciousness monitoring to enhance patient safety and advance our understanding of conscious states.