A sleep spindle detection algorithm that emulates human expert spindle scoring

• Published in: Journal of neuroscience methods Vol. 316; pp. 3 - 11
• Main Authors: Lacourse, Karine, Delfrate, Jacques, Beaudry, Julien, Peppard, Paul, Warby, Simon C.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.03.2019
• Subjects: Detector; Electroencephalography (EEG); Polysomnography (PSG); Sigma; Sleep; Sleep spindle; fastAvgBsl; A7absSigPow; IntegSpectPow; EEGbf; Sigma; FN; NREM; A7relSigPow; FP; WSC1950; Sleep spindle; A7sigmaCorr; Detectors tested – algorithm A; Electroencephalography (EEG); FFT; By-sample analysis/performance; slowRatio; EEGσ; PSA; By-event analysis/performance; AASM; A7sigmaCov; Polysomnography (PSG); PSG; EEG; slRatThresh; GS; OvT; OED; spm; Sleep; Detector; slowAvgBsl; IExp; SOS; NF; GCT; NG; WinL; TN; WSC110sub; TP; WinS
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2018.08.014

•The A7 (‘algorithm #7’) detector uses 4 parameters to identify spindles that are visible in the original EEG.•A7 emulates human spindles scoring by maximizing performance against a human gold standard.•The ‘context classifier’ filters out spindles that are not in a typical NREM spectral context.•Identifying visible spindles may be useful because they have a high signal-to-noise ratio. Sleep spindles are a marker of stage 2 NREM sleep that are linked to learning & memory and are altered by many neurological diseases. Although visual inspection of the EEG is considered the gold standard for spindle detection, it is time-consuming, costly and can introduce inter/ra-scorer bias. Our goal was to develop a simple and efficient sleep-spindle detector (algorithm #7, or ‘A7’) that emulates human scoring. ‘A7’ runs on a single EEG channel and relies on four parameters: the absolute sigma power, relative sigma power, and correlation/covariance of the sigma band-passed signal to the original EEG signal. To test the performance of the detector, we compared it against a gold standard spindle dataset derived from the consensus of a group of human experts. The by-event performance of the ‘A7’ spindle detector was 74% precision, 68% recall (sensitivity), and an F1-score of 0.70. This performance was equivalent to an individual human expert (average F1-score = 0.67). The F1-score of ‘A7’ was 0.17 points higher than other spindle detectors tested. Existing detectors have a tendency to find large numbers of false positives compared to human scorers. On a by-subject basis, the spindle density estimates produced by A7 were well correlated with human experts (r2 = 0.82) compared to the existing detectors (average r2 = 0.27). The ‘A7’ detector is a sensitive and precise tool designed to emulate human spindle scoring by minimizing the number of ‘hidden spindles’ detected. We provide an open-source implementation of this detector for further use and testing.