Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).

Tarek Lajnef

doi:10.3389/fninf.2017.00015

Abstract

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O’Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew’s coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.

Highlights

A good night’s rest is often made possible by an active brain that exhibits complex macro and micro-structures of electrical activity at various spatial and temporal scales (Iber et al, 2007; Carskadon and Dement, 2011)
By combining a discrete wavelet transform known as the TQWT (Selesnick, 2011a) with MCA, Spinky allows for the decomposition of the electroencephalographic signals (EEG) signal into transient (K-complex) and oscillatory components (Lajnef et al, 2015a)
The small sample data to score for training should emanate from N2 epochs, as this is the stage where the targeted events are most prominent

Summary

Introduction

A good night’s rest is often made possible by an active brain that exhibits complex macro and micro-structures of electrical activity at various spatial and temporal scales (Iber et al, 2007; Carskadon and Dement, 2011). Sleep EEG recordings contain characteristic micro-structures (i.e., short-lived stereotypical events) that are often considered to be hallmarks of sleeprelated cognitive processes and, in some cases, a sign of sleep anomalies. K-complexes and sleep spindles are some of the most prominent micro-events that are studied in sleep studies. Given that they mainly occur during the N2 sleep stage, spindles and K-complexes guide experts during their scoring of sleep stages, but they are thought to be key elements in the diagnosis of sleep disorders and the exploration of the functional role of sleep

Methods

Results

Conclusion