Computational NeuroscienceImproved spindle detection through intuitive pre-processing of electroencephalogram

- This study presents a spindle detection approach which relies on intuitive pre-processing of EEG.
- Techniques include time differentiation, background filtering via empirical mode decomposition and EOG based shortlisting.
- Standard approaches such as wavelets and Fourier transforms performed better when coupled with apt pre-processing.
- Average sensitivities of 96.14 and 92.85% and specificities of 87.59 and 84.85% for Fourier and Wavelets respectively.
- Inter-subject and inter-scorer tunability with data driven thresholds are validated.

BackgroundNumerous 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 methodThe 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/comparisonResults 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.