A Gradient Boosting Classifier-Based Approach for Automated Sleep Spindle Detection in Rat EEG Recordings

The electroencephalogram (EEG) is widely used to study brain activity, including sleep spindles, which are brief neural oscillations occurring during non-rapid eye movement sleep. Rats serve as valuable models for researching sleep disorders and neurological diseases. The manual detection of sleep spindles in EEG recordings is time-consuming and requires expert knowledge, driving the need for automated detection methods. However, most existing methods are designed for humans and cannot be directly applied to rodents due to differences in sleep spindle frequency, morphology, and amplitude. This study presents a gradient boosting classifier approach for detecting sleep spindles in rat EEG recordings. Left and right EEG activities from nine rats were utilized for training and validation, with an additional six rats used for independent testing. EEG recordings were segmented into 1-second epochs with 0.5-second overlap, and 18 features were estimated for classification. The proposed method achieved robust performance and reliably highlighted key predictive features, offering an efficient and reliable method for analyzing sleep spindle oscillations in rat EEG data.