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

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 man...

Full description

Bibliographic Details
Main Authors: Christian eO'Reilly, Tore eNielsen
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-06-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00353/full
id doaj-a8afd2879c9c43ba8b81663a5ffc6159
record_format Article
spelling doaj-a8afd2879c9c43ba8b81663a5ffc61592020-11-25T02:14:46ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612015-06-01910.3389/fnhum.2015.00353121774Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science toolsChristian eO'Reilly0Christian eO'Reilly1Christian eO'Reilly2Tore eNielsen3Tore eNielsen4McGill University - Montreal Neurological InstituteHôpital du Sacré-Coeur de MontréalUniversité de MontréalHôpital du Sacré-Coeur de MontréalUniversité de MontréalSleep 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 seldom posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00353/fullassessmentReliabilitySensitivitySleep Spindlestemporal resolutiongold standard
collection DOAJ
language English
format Article
sources DOAJ
author Christian eO'Reilly
Christian eO'Reilly
Christian eO'Reilly
Tore eNielsen
Tore eNielsen
spellingShingle Christian eO'Reilly
Christian eO'Reilly
Christian eO'Reilly
Tore eNielsen
Tore eNielsen
Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science tools
Frontiers in Human Neuroscience
assessment
Reliability
Sensitivity
Sleep Spindles
temporal resolution
gold standard
author_facet Christian eO'Reilly
Christian eO'Reilly
Christian eO'Reilly
Tore eNielsen
Tore eNielsen
author_sort Christian eO'Reilly
title Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science tools
title_short Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science tools
title_full Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science tools
title_fullStr Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science tools
title_full_unstemmed Automatic sleep spindle detection: Benchmarking with fine temporal resolution using open science tools
title_sort automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools
publisher Frontiers Media S.A.
series Frontiers in Human Neuroscience
issn 1662-5161
publishDate 2015-06-01
description 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 seldom posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.
topic assessment
Reliability
Sensitivity
Sleep Spindles
temporal resolution
gold standard
url http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00353/full
work_keys_str_mv AT christianeoreilly automaticsleepspindledetectionbenchmarkingwithfinetemporalresolutionusingopensciencetools
AT christianeoreilly automaticsleepspindledetectionbenchmarkingwithfinetemporalresolutionusingopensciencetools
AT christianeoreilly automaticsleepspindledetectionbenchmarkingwithfinetemporalresolutionusingopensciencetools
AT toreenielsen automaticsleepspindledetectionbenchmarkingwithfinetemporalresolutionusingopensciencetools
AT toreenielsen automaticsleepspindledetectionbenchmarkingwithfinetemporalresolutionusingopensciencetools
_version_ 1724899741902307328