Improved spindle detection through intuitive pre-processing of electroencephalogram.

TitleImproved spindle detection through intuitive pre-processing of electroencephalogram.
Publication TypeJournal Article
Year of Publication2014
AuthorsJaleel A, Ahmed B, Tafreshi R, Boivin DB, Streletz L, Haddad N
JournalJ Neurosci Methods
Date Published2014 Aug 15
KeywordsAlgorithms, Brain, Electroencephalography, Electrooculography, Eye Movement Measurements, Fourier Analysis, Humans, Models, Neurological, Probability, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Sleep

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.

Alternate JournalJ. Neurosci. Methods
PubMed ID24887741