2 research outputs found
Feature Extraction and Selection in Automatic Sleep Stage Classification
Sleep stage classification is vital for diagnosing many sleep related
disorders and Polysomnography (PSG) is an important tool in this regard.
The visual process of sleep stage classification is time consuming, subjective
and costly. To improve the accuracy and efficiency of the sleep stage
classification, researchers have been trying to develop automatic
classification algorithms.
The automatic sleep stage classification mainly consists of three steps:
pre-processing, feature extraction and classification. In this research work,
we focused on feature extraction and selection steps. The main goal of this
thesis was identifying a robust and reliable feature set that can lead to
efficient classification of sleep stages. For achieving this goal, three types of
contributions were introduced in feature selection, feature extraction and
feature vector quality enhancement.
Several feature ranking and rank aggregation methods were evaluated and
compared for finding the best feature set. Evaluation results indicated that
the decision on the precise feature selection method depends on the system
design requirements such as low computational complexity, high stability
or high classification accuracy. In addition to conventional feature ranking
methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder
(SSAE) was used for dimensionality reduction.
In feature extration area, new and effective features such as distancebased
features were utilized for the first time in sleep stage classification.
The results showed that these features contribute positively to the
classification performance. For signal quality enhancement, a loss-less EEG
artefact removal algorithm was proposed. The proposed adaptive algorithm
led to a significant enhancement in the overall classification accuracy