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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

(A) There was a great deal of overlap between Expert 1 and non-experts in terms of spindle duration (Cohen's d = 0.14), but less overlap with Expert 2 (Cohen's d = 0.85) or between Expert 2 and non-experts (Cohen's d = 0.63). Spindle duration of automatically detected spindles were generally shorter in duration than Expert 1 (Cohen's d = 1.12), Expert 2 (Cohen's d = 0.57), or non-experts (Cohen's d = 0.91). Spindle duration among visual identification methods (B–D) and between automatic and visual detection (E–G) were all highly inter-correlated (all p < 0.05).
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Figure 3: (A) There was a great deal of overlap between Expert 1 and non-experts in terms of spindle duration (Cohen's d = 0.14), but less overlap with Expert 2 (Cohen's d = 0.85) or between Expert 2 and non-experts (Cohen's d = 0.63). Spindle duration of automatically detected spindles were generally shorter in duration than Expert 1 (Cohen's d = 1.12), Expert 2 (Cohen's d = 0.57), or non-experts (Cohen's d = 0.91). Spindle duration among visual identification methods (B–D) and between automatic and visual detection (E–G) were all highly inter-correlated (all p < 0.05).

Mentions: The most apparent differences in the characteristics of spindles identified by the various visual scoring approaches were for spindle duration and amplitude. In general, Expert 1 and non-experts identified spindles with very similar distributions of durations (Cohen's d = 0.14) ranging from about 0.2–3 s in length (Figure 3A), whereas Expert 2 identified spindles in a more restricted range between about 0.5 and 2 s in length (Figure 3A), whose distribution overlapped less with Expert 1 (Cohen's d = 0.85) and the consensus of the non-experts (Cohen's d = 0.63). A similar pattern was observed for amplitude whereby Expert 1 tended to score more spindles with smaller amplitudes (Figure 4A) than Expert 2 (Cohen's d = 0.63), with the distribution of non-expert spindle amplitudes overlapping to a greater extent with Expert 1 (Cohen's d = 0.2) than Expert 2 (Cohen's d = 0.37), respectively (Figure 4A). By contrast, there was considerable overlap between visual scoring approaches for mean frequency (Figure 5A) between Experts 1 and 2 (Cohen's d = 0.08), Expert 1 and non-experts (Cohen's d = 0.16) and between Expert 2 and non-experts (Cohen's d = 0.23). In terms of mean frequency, however, from inspection of Figure 5A, it appears that non-experts tended to identify more spindles with a slower frequency than either Expert 1 or 2, perhaps due to mistakenly identifying brief arousals (i.e., alpha activity) as spindles.


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)

(A) There was a great deal of overlap between Expert 1 and non-experts in terms of spindle duration (Cohen's d = 0.14), but less overlap with Expert 2 (Cohen's d = 0.85) or between Expert 2 and non-experts (Cohen's d = 0.63). Spindle duration of automatically detected spindles were generally shorter in duration than Expert 1 (Cohen's d = 1.12), Expert 2 (Cohen's d = 0.57), or non-experts (Cohen's d = 0.91). Spindle duration among visual identification methods (B–D) and between automatic and visual detection (E–G) were all highly inter-correlated (all p < 0.05).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4585171&req=5

Figure 3: (A) There was a great deal of overlap between Expert 1 and non-experts in terms of spindle duration (Cohen's d = 0.14), but less overlap with Expert 2 (Cohen's d = 0.85) or between Expert 2 and non-experts (Cohen's d = 0.63). Spindle duration of automatically detected spindles were generally shorter in duration than Expert 1 (Cohen's d = 1.12), Expert 2 (Cohen's d = 0.57), or non-experts (Cohen's d = 0.91). Spindle duration among visual identification methods (B–D) and between automatic and visual detection (E–G) were all highly inter-correlated (all p < 0.05).
Mentions: The most apparent differences in the characteristics of spindles identified by the various visual scoring approaches were for spindle duration and amplitude. In general, Expert 1 and non-experts identified spindles with very similar distributions of durations (Cohen's d = 0.14) ranging from about 0.2–3 s in length (Figure 3A), whereas Expert 2 identified spindles in a more restricted range between about 0.5 and 2 s in length (Figure 3A), whose distribution overlapped less with Expert 1 (Cohen's d = 0.85) and the consensus of the non-experts (Cohen's d = 0.63). A similar pattern was observed for amplitude whereby Expert 1 tended to score more spindles with smaller amplitudes (Figure 4A) than Expert 2 (Cohen's d = 0.63), with the distribution of non-expert spindle amplitudes overlapping to a greater extent with Expert 1 (Cohen's d = 0.2) than Expert 2 (Cohen's d = 0.37), respectively (Figure 4A). By contrast, there was considerable overlap between visual scoring approaches for mean frequency (Figure 5A) between Experts 1 and 2 (Cohen's d = 0.08), Expert 1 and non-experts (Cohen's d = 0.16) and between Expert 2 and non-experts (Cohen's d = 0.23). In terms of mean frequency, however, from inspection of Figure 5A, it appears that non-experts tended to identify more spindles with a slower frequency than either Expert 1 or 2, perhaps due to mistakenly identifying brief arousals (i.e., alpha activity) as spindles.

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