A machine learning model for identifying cyclic alternating patterns in the sleeping brain

Electroencephalography (EEG) is a method to record the electrical signals in the brain. Recognizing the EEG patterns in the sleeping brain gives insights into the sleeping disorders. The dataset uploaded under consideration contains data points associated to numerous physiologies. There are particular patterns associated with the Non-Rapid Eye Movement (NREM) sleep cycle of the brain. This study attempts to generalize the detection of these patterns using a machine learning model. The proposed model uses additional feature engineering to incorporate sequential information for training a classifier to predict the occurrence of Cyclic Alternating Pattern (CAP) sequences in the sleep cycle, which are often associate with sleep disorders.