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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

Spindle characteristics over the course of the first four NREM periods, at frontal and parietal regions for fast and slow spindle types, including density (A), duration (B), amplitude (C) and frequency (D).
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Figure 8: Spindle characteristics over the course of the first four NREM periods, at frontal and parietal regions for fast and slow spindle types, including density (A), duration (B), amplitude (C) and frequency (D).

Mentions: Spindle characteristics over the course of NREM cycles and across frontal and parietal regions followed well-established patterns (Figure 8). A cycle (NREM cycle 1–4) × spindle type (fast, slow) × site (frontal, parietal) ANOVA for spindle density revealed a significant three-way interaction [F(3, 42) = 3.98, p = 0.014]. This was driven by a higher density of slow spindles (3.38, ±0.62) than fast spindles (1.16, ±0.54) at F3 as compared to a higher density of fast spindles (3.31, ±0.91) than slow spindles (1.52, ±0.69) at P3 [F(1, 14) = 149.62, p < 0.001]. Spindle density also differed across NREM cycles in a U-shaped pattern (Himanen et al., 2002), but more so for fast spindles than slow spindles, as indicated by a significant type by NREM cycle interaction [F(3, 42) = 4.74, p = 0.006].


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)

Spindle characteristics over the course of the first four NREM periods, at frontal and parietal regions for fast and slow spindle types, including density (A), duration (B), amplitude (C) and frequency (D).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 8: Spindle characteristics over the course of the first four NREM periods, at frontal and parietal regions for fast and slow spindle types, including density (A), duration (B), amplitude (C) and frequency (D).
Mentions: Spindle characteristics over the course of NREM cycles and across frontal and parietal regions followed well-established patterns (Figure 8). A cycle (NREM cycle 1–4) × spindle type (fast, slow) × site (frontal, parietal) ANOVA for spindle density revealed a significant three-way interaction [F(3, 42) = 3.98, p = 0.014]. This was driven by a higher density of slow spindles (3.38, ±0.62) than fast spindles (1.16, ±0.54) at F3 as compared to a higher density of fast spindles (3.31, ±0.91) than slow spindles (1.52, ±0.69) at P3 [F(1, 14) = 149.62, p < 0.001]. Spindle density also differed across NREM cycles in a U-shaped pattern (Himanen et al., 2002), but more so for fast spindles than slow spindles, as indicated by a significant type by NREM cycle interaction [F(3, 42) = 4.74, p = 0.006].

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