Deep Sleep
|
Project Summary
Automatic Sleep Stage Classification using Advanced Artificial Intelligence
Techniques
Sleep occupies significant part of human life. It is fundamental for
physical and mental health. Although most people think that sleep
is a passive and constant process, as a matter of fact, it is an active
state. Human body moves frequently during the night and the brain
is sometimes more active during sleep than in the period of the normal
wake state.
Healthy human sleep generally consists of two distinct stages with
independent functions known as Non-Rapid Eye Movement (NREM) and Rapid
Eye Movement (REM) sleep. In the ideal situation, NREM and REM states
alternate regularly, each cycle lasting 90 minutes on average. NREM
sleep accounts for 75-80% of sleep duration. According to American
Academy of Sleep Medicine (AASM), NREM is subdivided into three stages:
stage 1 or light sleep, stage 2 and stage 3 or Slow Wave Sleep (SWS).
Classification of sleep stages is vital for diagnosing many sleep
related disorders and Polysomnography (PSG) is an important tool in
this regards.
The automatic sleep stage classification mainly consists of three
steps: pre-processing, feature extraction and classification. The
pre-processing step includes artefact rejection and artefact correction.
In the feature extraction step, researchers try to compactly represent
PSG recordings by means of a feature vector. This feature vector should
be informative and non-redundant enough to ease the subsequent classification
step. Finally, in the classification step, the extracted feature vectors
are assigned to one of the five categories using a proper classifier.
Although much has been done for automatic sleep stage classification,
still there is lack of a method for efficient sleep monitoring which
can be considered reliable from medical expert’s point of view.
The aim of this project is to fill this gap by using advanced data
mining and artificial intelligence techniques.
Overall Objectives
The project will contribute to the investigating the potential
of advanced artificial intelligence methods in reliable automatic
sleep stage classification.
The specific objectives of the project include:
- De-noising the PSG recordings using adaptive methods without
distorting the original signal.
- Analysis the capability of traditional features from the consistency
and effectiveness point of view.
- Propose new feature extraction and selection scheme based on
advanced data mining techniques.
- Applying recent and advanced classification methods for achieving
more robust and accurate classification results compared to traditional
methods.
Research Topics
- Artificial Intelligence
- Deep Learning
- Adaptive De-noising
Partners
|
|
