Authors:
Ammar Hussain Khan
;
Ibrahim Onaran
;
Nuri Firat Ince
;
Mostafa Kaveh
;
Tacjana Friday
;
Mike Howell
;
Thomas Henry
and
Zhiyi Sha
Affiliation:
University of Minnesota, United States
Keyword(s):
Cyclic Alternating Pattern, Ambulatory EEG, Principal Component Analysis, Spatial PCA, Classification
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Monitoring and Telemetry
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time and Frequency Response
;
Time-Frequency Analysis
Abstract:
Cyclic Alternating Pattern (CAP) Occurs during Non-Rapid Eye Movement (NREM) Sleep and Is Exploited as a Neuro-Marker of Various Sleep Disorders. the CAP Is Build up from so Called a and B Phases Which Correspond to Widespread Synchronous and Regular Background Activities of EEG Respectively. Currently, These Phases Are Detected by Medical Experts through Visual Inspection, Thereby Limiting Their Potential to Be Used as a Gauge for Sleep Quality. This Paper Aims to Contribute to the Current Effort towards Automatic Detection of CAP Phases, so That Its Potential Can Be Improved in the Assessment of Sleep Quality. unlike Previous Research Where a Predefined Bipolar (and/or Monopolar) Channel Was Used for Automatic Detection, This Paper Explores the Use of a Two-Step Principal Component Analysis (PCA) in Spatial and Feature Domains to Extract Features from All 21 Recording Channels of Ambulatory EEG. Linear Discriminant Analysis (LDA) Was Used on the Extracted Features to Discriminate
Phase a and B. over a Five Subject Database, Our Algorithm Reached an Average Classification Accuracy over 86%, Whereas the Baseline Approach Resulted in an 80.3% Success Rate. These Results Indicate That the Two Step PCA Procedure Can Be Used Effectively to Extract Features from Ambulatory EEG towards Detection of CAP.
(More)