Journal Article

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Osorio-Forero, Alejandro, Rodríguez-Sotelo, Jose, Cirugeda-Roldán, Eva, Cuesta-Frau, David, Jiménez-Rodríguez, Alejandro, Peluffo, Diego

2014

Overview

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

• Publisher: MDPI AG
• Subject: Algorithms / Automation / Classification / Entropy / Feature extraction / feature selection / Manuals / Pattern analysis / Q-α clustering / relevance analysis / Segmentation / signal entropy / Sleep / sleep stages