Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation.

Abstract

Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: 1) the limited use of automated spindle detection in ADOC; 2) the difficulty in identifying low-frequency spindles in patient populations; and 3) the lack of effective tools for quantitatively analyzing the relationship between spindle density and patient outcomes. To address these challenges, we propose a novel Deep Learning-Augmented algorithm for automated sleep spindle detection in ADOC patients. This method combines Convolutional Neural Networks with decision tree-assisted validation, using wavelet transform principles to enhance detection accuracy and sensitivity, especially for the slow spindles commonly found in ADOC patients. Our approach not only demonstrates superior performance and reliability but also has the potential to significantly improve diagnostic precision and guide treatment strategies when integrated into clinical practice. Our algorithm was evaluated on the Montreal Archive of Sleep Studies - Session 2 (MASS SS2, n = 19), achieving average F1 scores of 0.798 and 0.841 compared to annotations from two experts. On a self-recorded dataset from ADOC patients (n = 24), it achieved an F1 score of 0.745 compared to expert annotations. Additionally, our analysis using the Spearman correlation coefficient revealed a moderate positive correlation between sleep spindle density and 28-day Glasgow Outcome Scale scores in ADOC patients. This suggests that spindle density could serve as a prognostic marker for predicting clinical outcomes and guiding personalized patient care.