A machine learning model for identifying cyclic alternating patterns in the sleeping brain
This work addresses sleep disorder diagnosis by automating CAP detection in EEG data, but it is incremental as it builds on existing methods with feature engineering.
The study tackled the problem of detecting Cyclic Alternating Pattern (CAP) sequences in NREM sleep EEG data, which are linked to sleep disorders, by developing a machine learning model with feature engineering to incorporate sequential information, achieving improved detection accuracy.
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 understanding of sleeping disorders. The dataset under consideration contains EEG data points associated with various physiological conditions. This study attempts to generalize the detection of particular patterns associated with the Non-Rapid Eye Movement (NREM) sleep cycle of the brain 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 associated with sleep disorders.