Blind source separation for OSAS: data extraction

In this research, the electroencephalogram (EEG) signals for sleep apnea were extracted and processed using Blind Source Separation approach. To identify the EEG features, 13 Independent Component Analysis methods were adopted to analyse the data extraction performance. All the EEG signals on Obstructive Sleep Apnea Syndrome (OSAS) were recorded using 10-20 international electrode placement system. The experiment was conducted based on a 20-min sleep recording during rapid eye movement sleep characterized by rapid saccadic movements of the eyes and non-rapid eye movement sleep. Seven electrode positions were identified to record the EEG signals, with a sampling time of 100 Hz. The result was investigated using the proposed 13 ICA algorithms to understand the important EEG signals and features for every process. The wavelets denoising results were obtained to evaluate the robustness of the proposed wavelets denoising algorithms. According to the performance analysis, the proposed wavelets denoising technique could be used to investigate the recorded EEG signals with lower signal amplitude.