Multitaper-based method for automatic k-complex detection in human sleep EEG

Abstract In this paper, we propose a novel method for automatic k-complex (KC) detection in human sleep EEG, named MT-KCD. KCs are slow oscillations in the EEG signal characterized by a well-delineated, negative, sharp waves immediately followed by a positive component standing out from the background, with high-amplitude and total duration ≥ 0.5 s. Among the important aspects of the KC are its homeostatic and reactive functions in the brain, functioning as a sleep protection mechanism, and its practical use as a marker of N2 sleep stage during sleep studies. Given the importance of the KC, and the effort required from human experts to analyze EEG recordings visually, some recent research works have proposed automatic methods for KC detection. In comparison with existing methods, a key feature and novelty of MT-KCD is the use of multitaper spectral analysis to pre-process the EEG signal and automatically extract candidate KCs from it (characterized as 0-4 Hz power concentrations standing out from the background). After extraction, candidates are accepted/rejected depending on time domain characteristics (peak-to-peak amplitude ≥ 75 µV, duration ≤ 2 s). The method overall time complexity is O ( N · log N ) . Regarding effectiveness, we have evaluated MT-KCD by using a public KC database (DREAMS) consisting of ten polysomnographic recordings of healthy patients (6 female and 4 male subjects with age range 20–47 years) partially annotated by two experts. Results have shown that MT-KCD improves detection metrics, especially F1 and F2 scores (harmonic averages of recall and precision), when compared to existing methods. Besides, improving F1 and F2 scores, MT-KCD also contributes to the automatic analysis of sleep EEG multitaper spectrograms, a technique recently proposed by researchers in the area of sleep studies as a complement to the traditional hypnogram (sleep stages diagram).