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Front Hum Neurosci. 2015 Jun 24;9:353. doi: 10.3389/fnhum.2015.00353. eCollection 2015.

Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools.

Author information

1
MEG Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada ; Dream and Nightmare Laboratory, Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal Montreal, QC, Canada ; Département de Psychiatrie, Université de Montréal Montreal, QC, Canada.
2
Dream and Nightmare Laboratory, Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal Montreal, QC, Canada ; Département de Psychiatrie, Université de Montréal Montreal, QC, Canada.

Abstract

Sleep spindle properties index cognitive faculties such as memory consolidation and diseases such as major depression. For this reason, scoring sleep spindle properties in polysomnographic recordings has become an important activity in both research and clinical settings. The tediousness of this manual task has motivated efforts for its automation. Although some progress has been made, increasing the temporal accuracy of spindle scoring and improving the performance assessment methodology are two aspects needing more attention. In this paper, four open-access automated spindle detectors with fine temporal resolution are proposed and tested against expert scoring of two proprietary and two open-access databases. Results highlight several findings: (1) that expert scoring and polysomnographic databases are important confounders when comparing the performance of spindle detectors tested using different databases or scorings; (2) because spindles are sparse events, specificity estimates are potentially misleading for assessing automated detector performance; (3) reporting the performance of spindle detectors exclusively with sensitivity and specificity estimates, as is often seen in the literature, is insufficient; including sensitivity, precision and a more comprehensive statistic such as Matthew's correlation coefficient, F1-score, or Cohen's κ is necessary for adequate evaluation; (4) reporting statistics for some reasonable range of decision thresholds provides a much more complete and useful benchmarking; (5) performance differences between tested automated detectors were found to be similar to those between available expert scorings; (6) much more development is needed to effectively compare the performance of spindle detectors developed by different research teams. Finally, this work clarifies a long-standing but only seldomly posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.

KEYWORDS:

assessment; automatic detection; gold standard; reliability; sensitivity; sleep spindles; temporal resolution

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