Abstract
Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.
Highlights
Sleep spindles are brief, distinct bursts of brain activity in the sigma frequency range (11–16 Hz) as measured by electroencephalography (EEG)
We have focused on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context with the resultant Pareto fronts as the basis for deriving more commonly accepted performance evaluation metrics such as precision (P), recall (R), and F1-scores
For detectors d4-d9 with more than 10 parameters, the population size was ascertained by 10 times the number of parameters and the number of generations was empirically determined by the population size plus 50–100 to ensure convergence to Pareto front solutions for the DREAMS database
Summary
Sleep spindles are brief (at least 0.5 s), distinct bursts of brain activity in the sigma frequency range (11–16 Hz) as measured by electroencephalography (EEG). They are characterized by the waxing and waning shape of a spindle. Along with K-complexes they are key EEG features used to define non-rapid eye movement (NREM) stage 2 sleep in sleep scoring according to AASM (Iber et al, 2007) guidelines. These oscillations are of great biological and clinical interests because they. For the aforementioned reasons, detecting sleep spindles, and scoring their properties have become an important task in both research and clinical settings
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