Background: Numerous signal processing techniques have been proposed for automated spindle detection on EEG recordings with varying degrees of success. While the latest techniques usually introduce computational complexity and/or vagueness, the conventional techniques attempted in literature have led to poor results. This study presents a spindle detection approach which relies on intuitive pre-processing of the EEG prior to spindle detection, thus resulting in higher accuracy even with standard techniques.
New method: The pre-processing techniques proposed include applying the derivative operator on the EEG, suppressing the background activity using Empirical Mode Decomposition and shortlisting candidate EEG segments based on eye-movements on the EOG.
Results/comparison: Results show that standard signal processing tools such as wavelets and Fourier transforms perform much better when coupled with apt pre-processing techniques. The developed algorithm also relies on data-driven thresholds ensuring its adaptability to inter-subject and inter-scorer variability. When tested on sample EEG segments scored by multiple experts, the algorithm identified spindles with average sensitivities of 96.14 and 92.85% and specificities of 87.59 and 84.85% for Fourier transform and wavelets respectively. These results are found to be on par with results obtained by other recent studies in this area.
Keywords: Algorithms; EEG; EOG; Fourier transform; Spindle detection; Wavelets.
Copyright © 2014 Elsevier B.V. All rights reserved.