Automatic Detection of Sleep Spindles and its Application in Patients with Acute Disorders of Consciousness

Sleep spindles play an important role in human sleep and are considered to have great significance in predicting the prognosis of patients with acute disorders of consciousness (ADOC). Although previous studies have achieved high performance in the automatic detection of sleep spindles in normal subjects, the application in ADOC is very limited, and several challenges remain: 1) how to effectively detect patients' spindles that may decrease in frequency; 2) how to improve the generality of the method to detect more electroencephalogram (EEG) events, such as K-complexes; and 3) how to intuitively reflect the relationship between patients' spindle density and prognosis. To address the above challenges, we propose SpindleCatcher, a deep learning strategy to detect sleep spindles, and design an experiment to investigate the correlation between spindle density and prognosis in ADOC. SpindleCatcher jointly predicts the locations and durations of spindles in EEG, using a convolutional neural network to extract features from raw EEG signals and two modules for localization and classification tasks. Specifically, a frequency attention module is applied to better focus on signals in the desired frequency ranges to improve the performance of ADOC spindle detection. SpindleCatcher can also detect other EEG events, such as K-complexes. Experiments demonstrate that the proposed method exceeds the baseline methods on spindle detection with an overall recall of 0.817 and F1 score of 0.794 on the publicly available MASS2 dataset and an overall recall of 0.707 and F1 score of 0.681 on the patient dataset. The correlation experiment shows that there may be a strong positive correlation between the sleep spindle density of ADOCs and their outcomes.