Sleep-stage scoring plays an important role in analyzing the sleep patterns of people. Studies have revealed that Intensive Care Unit (ICU) patients do not usually get enough quality sleep, and hence, analyzing their sleep patterns is of increased importance. Due to the fact that sleep data are usually collected from a number of Electroencephalogram (EEG), Electromyogram (EMG) and Electrooculography (EOG) channels, the feature set size can become large, which may affect the development of on-line scoring systems. Hence, a dimensionality reduction step is needed. One of the powerful dimensionality reduction approaches is based on the concept of Linear Discriminant Analysis (LDA). Unlike existing variants of LDA, this paper presents a new method that considers the fuzzy nature of input measurements while preserving their local structure. Practical results indicate the significance of preserving the local structure of sleep data, which is achieved by the proposed method, and hence attaining superior results to other dimensionality reduction methods.
['Rami N. Khushaba', 'Rosalind Elliott', 'Akram AlSukker', 'Ahmed Al-Ani', 'Sharon McKinley']
Orthogonal Locality Sensitive Fuzzy Discriminant Analysis in Sleep-Stage Scoring