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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

Automated spindle detection method processing steps. (A) Step 1, the EEG was filtered using a high pass 0.3 Hz filter, low pass 35 Hz filter, and bad data and artifact was identified. (B) Step 2, the EEG was transformed using complex demodulation (CD), producing a new time series of instantaneous magnitude (μV2) in the frequency band of interest (e.g., 11–16 Hz). (C) Step 3, the CD time series was normalized to Z-scores calculated from a 60-s sliding window about each data point. Spindle onsets were detected when Z > 2.33 (i.e., 99th percentile). To more accurately measure the entire length of the spindle, the onset was adjusted to be the first point at which Z = 0.5 prior to the amplitude threshold Z, and the offset as the first point at which Z = 0.5 after the amplitude threshold Z. Figure reproduced from Fogel et al. (2014b).
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Figure 1: Automated spindle detection method processing steps. (A) Step 1, the EEG was filtered using a high pass 0.3 Hz filter, low pass 35 Hz filter, and bad data and artifact was identified. (B) Step 2, the EEG was transformed using complex demodulation (CD), producing a new time series of instantaneous magnitude (μV2) in the frequency band of interest (e.g., 11–16 Hz). (C) Step 3, the CD time series was normalized to Z-scores calculated from a 60-s sliding window about each data point. Spindle onsets were detected when Z > 2.33 (i.e., 99th percentile). To more accurately measure the entire length of the spindle, the onset was adjusted to be the first point at which Z = 0.5 prior to the amplitude threshold Z, and the offset as the first point at which Z = 0.5 after the amplitude threshold Z. Figure reproduced from Fogel et al. (2014b).

Mentions: EEG processing was carried out using EEGlab (V13) and Matlab (R2014a) (Figure 1) on the same data set (see Section Participants and EEG Data Set) using the same EEG channel (C3) as the expert and non-expert scorers. Thus, the validation between automated detection and visual raters is limited to NREM2 sleep from a single central (C3) derivation. Spindles were also detected from additional channels at frontal (F3) and parietal (P3) sites in both NREM2 and SWS across the first four NREM cycles to further explore the characteristics of the automatically detected spindles, in order to provide additional validation of known topographic distribution (Werth et al., 1997; Zeitlhofer et al., 1997), temporal patterns (Werth et al., 1997; De Gennaro et al., 2000) and the characteristics (Bódizs et al., 2009) of spindles. Prior to detection, the EEG was low-pass filtered at 35 Hz. Movement artifact was detected from the EMG channel (highpass filtered at 10 Hz) when the second order derivative of the signal exceeded 20 μV/ms. The EEG was marked as “bad data” ±3 s about the detected movement.


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)

Automated spindle detection method processing steps. (A) Step 1, the EEG was filtered using a high pass 0.3 Hz filter, low pass 35 Hz filter, and bad data and artifact was identified. (B) Step 2, the EEG was transformed using complex demodulation (CD), producing a new time series of instantaneous magnitude (μV2) in the frequency band of interest (e.g., 11–16 Hz). (C) Step 3, the CD time series was normalized to Z-scores calculated from a 60-s sliding window about each data point. Spindle onsets were detected when Z > 2.33 (i.e., 99th percentile). To more accurately measure the entire length of the spindle, the onset was adjusted to be the first point at which Z = 0.5 prior to the amplitude threshold Z, and the offset as the first point at which Z = 0.5 after the amplitude threshold Z. Figure reproduced from Fogel et al. (2014b).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Automated spindle detection method processing steps. (A) Step 1, the EEG was filtered using a high pass 0.3 Hz filter, low pass 35 Hz filter, and bad data and artifact was identified. (B) Step 2, the EEG was transformed using complex demodulation (CD), producing a new time series of instantaneous magnitude (μV2) in the frequency band of interest (e.g., 11–16 Hz). (C) Step 3, the CD time series was normalized to Z-scores calculated from a 60-s sliding window about each data point. Spindle onsets were detected when Z > 2.33 (i.e., 99th percentile). To more accurately measure the entire length of the spindle, the onset was adjusted to be the first point at which Z = 0.5 prior to the amplitude threshold Z, and the offset as the first point at which Z = 0.5 after the amplitude threshold Z. Figure reproduced from Fogel et al. (2014b).
Mentions: EEG processing was carried out using EEGlab (V13) and Matlab (R2014a) (Figure 1) on the same data set (see Section Participants and EEG Data Set) using the same EEG channel (C3) as the expert and non-expert scorers. Thus, the validation between automated detection and visual raters is limited to NREM2 sleep from a single central (C3) derivation. Spindles were also detected from additional channels at frontal (F3) and parietal (P3) sites in both NREM2 and SWS across the first four NREM cycles to further explore the characteristics of the automatically detected spindles, in order to provide additional validation of known topographic distribution (Werth et al., 1997; Zeitlhofer et al., 1997), temporal patterns (Werth et al., 1997; De Gennaro et al., 2000) and the characteristics (Bódizs et al., 2009) of spindles. Prior to detection, the EEG was low-pass filtered at 35 Hz. Movement artifact was detected from the EMG channel (highpass filtered at 10 Hz) when the second order derivative of the signal exceeded 20 μV/ms. The EEG was marked as “bad data” ±3 s about the detected movement.

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