Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness

The abstract details a compelling application of deep neural networks to address a profoundly complex and ethically sensitive domain: the assessment of consciousness in patients with disorders of consciousness (DOC). From an AI philosophical perspective, this work operates at the fascinating intersection of observable biological phenomena and the elusive nature of subjective experience.

The proposed CBASleepNet model meticulously deconstructs the problem. First, it employs a sophisticated Neural Networks architecture—combining Convolutional Neural Networks (CNNs) for raw signal feature extraction from electroencephalogram (EEG) and electrooculogram (EOG), and Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism for learning temporal sleep patterns—to achieve automatic sleep staging. This initial step is a robust technical solution for a well-defined pattern recognition task in Healthcare.

The critical philosophical pivot occurs in the second stage: using these automated sleep staging results to extract "consciousness-related sleep features" which are then fed into a Support Vector Machine (SVM) classifier to assess consciousness. Here, AI is not attempting to explain consciousness, but rather to correlate its observable physiological markers (specifically, sleep structure alterations) with clinically defined states of Consciousness (MCS vs. VS/UWS). This pragmatic approach highlights AI's strength in identifying subtle, complex patterns that might elude human observers or be subject to inter-rater variability.

The impressive 81.8% accuracy in differentiating MCS from VS/UWS patients underscores the potential for such AI-driven tools to provide more objective and consistent assessments in situations where human judgment can be challenging and deeply impactful. However, this raises important philosophical questions about Subjectivity. While the AI classifies based on objective physiological data, the underlying construct it aims to assess—consciousness—is fundamentally a subjective phenomenon. The model assesses markers associated with consciousness, not consciousness itself directly. This distinction is crucial. Does a better classification of "consciousness-related features" bring us closer to understanding the subjective experience of these patients, or does it merely refine our categorization of observable states?

Ultimately, this research exemplifies how advanced AI can serve as a powerful diagnostic aid in Healthcare, offering a quantitative lens through which to approach deeply qualitative and subjective human conditions. It foregrounds the ongoing challenge of bridging the gap between objective data and subjective experience, a perennial concern in the philosophy of mind.