Bódizs R*, Körmendi J, Rigó P, Lázár AS : The individual adjustment method of sleep spindle analysis: Methodological improvements and roots in the fingerprint paradigm. J. Neurosci. Methods 178(1) 205-213. 2009 – 1. Introduction

1. Introduction

Sleep spindles are usually defined as groups of 12–15 Hz sinusoidal electroencephalogram (EEG) waves occurring mainly during stage 2 non-rapid eye movement (NREM) sleep but occasionally appearing in stages 3 and 4 sleep as well (De Gennaro and Ferrara, 2003). There is growing neurophysiological knowledge regarding the nature of neural mechanisms generating sleep spindles, suggesting the role of hyperpolarization-rebound sequences in thalamocortical relay cells triggered, grouped and synchronized by cortico-cortical networks (Steriade, 2003). Moreover, there is a high-degree of interindividual difference in sleep spindle features accompanied by a remarkable intraindividual (night-to-night) stability (Silverstein and Levy, 1976; Gaillard and Blois, 1981; Werth et al., 1997; Tan et al., 2000; De Gennaro et al., 2005). The NREM sleep EEG power spectra at the 8 to 16 Hz frequency covering alpha and spindle activities is characterized by an individual profile, which is stable over time, resistant to experimental perturbations and strongly influenced by genetic factors (De Gennaro et al., 2008). A distinction of slower and faster sleep spindles based on frequency and topography was given by Gibbs and Gibbs (1950) and confirmed by studies using modern techniques like low-resolution electromagnetic tomography (Anderer et al., 2001), magnetoencephalography (Urakami, 2008), electrocorticography (Nakamura et al., 2003) and functional magnetic resonance imaging (Schabus et al., 2007). The frequency of slow spindles mostly corresponds to the alpha frequency range and detailed EEG studies suggest the possibility that slow spindles are anterior peaks of alpha activity during NREM sleep (De Gennaro and Ferrara, 2003). Given these evidences it is quite surprising that most of the methods of sleep spindle analysis are ultimately still based on, validated by, and tied to visual detection of spindles performed by experienced human scorers. By accepting visual scoring as the final test of automatic sleep spindle analysis one is implicitly assuming that human pattern recognition capacities are still superior to computer-based methods of spindle detection or that modern neurophysiological knowledge did not influence the definition of sleep spindles. Since we do not agree with these assumptions, we developed an improved method of sleep spindle analysis, which is a modified version of our previously published one (Bódizs et al., 2005). Our main starting points in the development of our method were the followings:

• 1. Sleep spindles are groups of waves in the 9–16 Hz range lasting at least 0.5 s and appearing in NREM sleep EEG records (De Gennaro and Ferrara, 2003; De Gennaro et al., 2005).
• 2. The exact spectral content of sleep spindles is individual-specific. In humans it emerges as an individually stable trait-like feature characterized most often by two distinct spectral peaks with different topography and sleep cycle dynamics (Werth et al., 1997; De Gennaro et al., 2005; Buckelmüller et al., 2006). There is an exceptionally low channel-by-channel, cycle-by-cycle and nightby-night variation in the individual-specific frequency bands delimiting the slow- and fast sleep spindles (Werth et al., 1997).

Based on these statements we defined sleep spindles as those segments of the NREM sleep EEG which last at least 0.5 s and contribute to one of the two individual-specific spectral peaks observed in the 9–16 Hz range. By accepting this definition our aim was to:

• 1. define the individual-specific spectral peaks in the 9–16 Hz range;
• 2. calculate the individual- and derivation-specific amplitude criteria for the two spindle types separately;
• 3. perform an adequate bandpass-filtering and to obtain the precise envelope curves in the individual-specific frequency ranges;
• 4. obtain not only the density, but also the mean amplitude and mean duration of the two sleep spindle types for each subject and each EEG derivation;
• 5. validate our method by testing previously established relationships:
  • (a) topographical difference between slow- and fast sleep spindles (anterior versus posterior predominance of slow- and fast sleep spindles, respectively);
  • (b) declining trend of slow spindling and increasing trend of fast spindling over consecutive sleep cycles;
  • (c) drop of sleep spindles with ageing and its interaction with sleep cycle effects;
• 6. compare the output of our automatic sleep spindle detection technique with the visual procedure performed by a trained expert.

Our previously published method (Bódizs et al., 2005) was modified in accordance with our sleep spindle definition and with new developments in the field. As regarding methodological improvements we introduced the zero-padding of EEG segments prior to fast Fourier transformation (FFT), as this was shown to be a reliable method of estimating the dominant spindle frequencies (Huupponen et al., 2006). Moreover, bandpa ss-filtering was based on Gauss-filters instead of Butterworth ones. And lastly we did not introduce any ad hoc correction in the amplitude criteria, but calculated a precise envelope of the filtered signals.

We hypothesized that the individual adjustment method (IAM) of sleep spindle analysis, which is an operationalization and application of our sleep spindle definition on human sleep EEG records:

• 1. results in spindle detections, which behave as slow- and fast sleep spindles in terms of topography, sleep cycle effects and age;
• age; 2. results in sleep spindles with an individual-specific spectral content paralleling the individual fingerprints of sleep EEG spectra, but being also articular in terms of the individual-specific spectral peaks;
• 3. is much more sensitive than visual detection performed by human experts (detect more spindles than humans), but the extra-spindles detected by the IAM:
  • (a) share the individual-specific spectral content unlike those visually detected spindles which are not covered by IAM;
  • (b) are characterized by an amplitude spectrum exceeding the average amplitude spectrum of the whole EEG segment (spindle + no-spindle) in terms of individual-specific spindle- peaks unlike those visually detected spindles which are not covered by IAM.