Combining time-frequency and spatial information for the detection of sleep spindles.
EEG sleep spindles are short (0.5-2.0 s) bursts of activity in the 11-16 Hz band occurring during non-rapid eye movement (NREM) sleep. This sporadic activity is thought to play a role in memory consolidation, brain plasticity, and protection of sleep integrity. Many automatic detectors have been proposed to assist or replace experts for sleep spindle scoring. However, these algorithms usually detect too many events making it difficult to achieve a good tradeoff between sensitivity (Se) and false detection rate (FDr). In this work, we propose a semi-automatic detector comprising a sensitivity phase based on well-established criteria followed by a specificity phase using spatial and spectral criteria. In the sensitivity phase, selected events are those which amplitude in the 10-16 Hz band and spectral ratio characteristics both reject a null hypothesis (p < 0.1) stating that the considered event is not a spindle. This null hypothesis is constructed from events occurring during rapid eye movement (REM) sleep epochs. In the specificity phase, a hierarchical clustering of the selected candidates is done based on events' frequency and spatial position along the anterior-posterior axis. Only events from the classes grouping most (at least 80%) spindles scored by an expert are kept. We obtain Se = 93.2% and FDr = 93.0% in the first phase and Se = 85.4% and FDr = 86.2% in the second phase. For these two phases, Matthew's correlation coefficients are respectively 0.228 and 0.324. Results suggest that spindles are defined by specific spatio-spectral properties and that automatic detection methods can be improved by considering these features.
Montreal Neurological Institute, McGill University Montreal, QC, Canada ; Département de Psychiatrie, Université de Montréal Montreal, QC, Canada ; Dream and Nightmare Laboratory, Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur Montreal, QC, Canada.
Christian
C
O'Reilly
Laboratoire PhysNum, École de Technologie Supérieure, Centre de Recherches Mathématiques Montreal, QC, Canada.
Jonathan
J
Godbout
Département de Psychologie, Université de Montréal Montreal, QC, Canada ; Chronobiology Laboratory, Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur Montreal, QC, Canada.
Julie
J
Carrier
Laboratoire PhysNum, École de Technologie Supérieure, Centre de Recherches Mathématiques Montreal, QC, Canada.
Jean-Marc
JM
Lina
detection; electroencephalography; hierarchical clustering; machine learning; pattern recognition; sleep; sleep spindles; time-frequency