Improved spindle detection through intuitive pre-processing of electroencephalogram

Abdul Jaleel, Beena Ahmed, Reza Tafreshi, Diane B. Boivin, Leopold Streletz, Naim Haddad

Research output: Contribution to journalArticle

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalJournal of Neuroscience Methods
Publication statusPublished - 15 Aug 2014



  • Algorithms
  • EEG
  • EOG
  • Fourier transform
  • Spindle detection
  • Wavelets

ASJC Scopus subject areas

  • Neuroscience(all)

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