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Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization.

Ray LB, Sockeel S, Soon M, Bore A, Myhr A, Stojanoski B, Cusack R, Owen AM, Doyon J, Fogel SM - Front Hum Neurosci (2015)

Bottom Line: Spindles were automatically detected in 15 young healthy subjects.These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives.This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

View Article: PubMed Central - PubMed

Affiliation: Brain and Mind Institute, Western University London, ON, Canada.

ABSTRACT
A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing "background" sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11-16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

No MeSH data available.


Related in: MedlinePlus

Histogram of mean spindle frequencies at frontal and parietal sites during NREM2 (A) and SWS (B). Fast spindles predominated parietal regions, whereas slow spindles predominated frontal regions.
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Figure 7: Histogram of mean spindle frequencies at frontal and parietal sites during NREM2 (A) and SWS (B). Fast spindles predominated parietal regions, whereas slow spindles predominated frontal regions.

Mentions: Consistent with previous reports (Zeitlhofer et al., 1997) Figure 7 reveals that a greater number of faster frequency spindles predominated parietal regions whereas a greater number of slower frequency spindles predominated frontal regions in both NREM2 (Figure 7A, Cohen's d = 0.43) and SWS (Figure 7B, Cohen's d = 0.78). This dissociation was supported by significant spindle type (fast, slow) × site (frontal, parietal) ANOVAs on automatically detected spindle density in NREM2 and SWS, which revealed that fast spindles predominated parietal regions as compared to slow spindles at frontal regions in both NREM2 [F(1, 14) = 149.62, p < 0.001], and SWS [F(1, 14) = 194.19, Table 4].


Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization.

Ray LB, Sockeel S, Soon M, Bore A, Myhr A, Stojanoski B, Cusack R, Owen AM, Doyon J, Fogel SM - Front Hum Neurosci (2015)

Histogram of mean spindle frequencies at frontal and parietal sites during NREM2 (A) and SWS (B). Fast spindles predominated parietal regions, whereas slow spindles predominated frontal regions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585171&req=5

Figure 7: Histogram of mean spindle frequencies at frontal and parietal sites during NREM2 (A) and SWS (B). Fast spindles predominated parietal regions, whereas slow spindles predominated frontal regions.
Mentions: Consistent with previous reports (Zeitlhofer et al., 1997) Figure 7 reveals that a greater number of faster frequency spindles predominated parietal regions whereas a greater number of slower frequency spindles predominated frontal regions in both NREM2 (Figure 7A, Cohen's d = 0.43) and SWS (Figure 7B, Cohen's d = 0.78). This dissociation was supported by significant spindle type (fast, slow) × site (frontal, parietal) ANOVAs on automatically detected spindle density in NREM2 and SWS, which revealed that fast spindles predominated parietal regions as compared to slow spindles at frontal regions in both NREM2 [F(1, 14) = 149.62, p < 0.001], and SWS [F(1, 14) = 194.19, Table 4].

Bottom Line: Spindles were automatically detected in 15 young healthy subjects.These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives.This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

View Article: PubMed Central - PubMed

Affiliation: Brain and Mind Institute, Western University London, ON, Canada.

ABSTRACT
A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing "background" sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11-16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

No MeSH data available.


Related in: MedlinePlus