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Sleep stage classification using LightGBM: Exploring feature impact in depressive and healthy participants: A Quasi-experimental study (Preprint)


 BACKGROUND
 The development of methods that efficiently and automatically classify sleep stages was a crucial milestone for the effective diagnosis and treatment of sleep-related disorders. However, the effectiveness of new classification methods, for instance, Light Gradient Boosting Machine (LightGBM) and whether these classification models can be applied to all types of participants remain unexplored
 
 
 OBJECTIVE
 The objective of our study was to compare the performance of three models of sleep stage classification (Support Vector Machine (SVM), Random Forest (RF), and (LightGBM), between patients with Depressive Disorder (DD) and healthy participants. We explored and compared the major features of the two groups.
 
 
 METHODS
 We recruited 40 participants and divided them into the DD group and the healthy control (HC) group. Overnight polysomnography (PSG) recordings were obtained. We extracted features from the PSG data as well as participants’ demographic and psychological information including; age, sex, body mass index (BMI), and Hamilton Depression Scale scores. The performance of LightGBM, SVM, and RF was evaluated using 10-fold cross-validation. Python packages were used for classification.
 
 
 RESULTS
 We found that the classification performance accuracy of LightGBM (74.7%, DD; 82.11%, HC) was superior to that of SVM (73.7%, DD; 80.8%, HC) and RF (71.9%, DD; 79.0%, HC), and the classification accuracy for the HC group was higher than that of the DD group. However, after including additional demographic features in the analysis, the classification performance of the DD group (from 74.7% to 76.6%) was greater than that of the HC group (from 82.11% to 82.6%). Feature ranking results revealed that BMI and Hamilton Depression Scale scores were the most crucial features for sleep stage classification in the DD group.
 
 
 CONCLUSIONS
 The findings provide great insights for nurses and clinicians in managing patients with DD, particularly those with sleep-related conditions. BMI and Hamilton Depression Scale scores should be considered when predicting sleep stages.
