Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Abstract

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

Heterogeneity in the links between sleep arousals, amyloid-β, and cognition

“…Sleep stage scoring was performed in 30-second windows using a validated algorithm (ASEEGA, Physip) ( 33 , 34 ). Automatic arousal detection was then computed as it is objective and reproducible, and because it saves time ( 35 ). We used an individually tailored validated algorithm based on the American Academy of Sleep Medicine (AASM) definition ( 12 ) of arousal but without using sleep stage information.…”

“…Events composed of at least 3 consecutive 1s epochs with changes in EEG frequencies higher than twice the local median and one median of the whole recording for that frequency band were considered as arousals. For detailed explanations on the method, see (35).…”

Timely sleep coupling: spindle-slow wave synchrony is linked to early amyloid-β burden and predicts memory decline

“…Recordings were scored for sleep stages in 30s windows using a validated automatic algorithm (ASEEGA, Physip, Paris, France) 44,45 . Automatic arousal and artefact detection 46,47 was performed in order to remove EEG segments containing artefacts and arousals from further analysis.…”

Heterogeneity in spontaneous sleep arousals: positive and negative links with early amyloid-beta and cognition

“…Sleep stage scoring was performed in 30s windows using a validated algorithm (ASEEGA, Physip, Paris, France) 38 . Automatic arousal detection was then computed as it is objective, reproducible and time-saving 39 . We used an individually tailored algorithm based on the American Academy of Sleep Medicine (AASM) de nition 18 .…”

“…Events composed of at least 3 consecutive 1s epochs with changes in EEG frequencies higher than twice the local median and one median of the whole recording for that frequency band were considered as arousals. For detailed explanations on the method, see 39 .…”