Computes per-channel signal quality metrics for electrodermal activity data, including basic statistics, flatline detection, artifact estimation, and an overall quality score.
Value
A data.frame with one row per channel and the following columns:
- channel
Channel label
- mean_sc
Mean skin conductance
- sd_sc
Standard deviation of skin conductance
- min_sc
Minimum skin conductance
- max_sc
Maximum skin conductance
- pct_negative
Percentage of samples <= 0
- pct_flatline
Percentage of signal in flatline segments (runs of consecutive near-zero differences longer than 1 second)
- pct_artifact
Percentage of gradient-based artifact samples
- snr_db
Estimated signal-to-noise ratio in dB (capped at 60)
- quality_score
Overall quality score from 0 to 100
- quality_label
"good" (>= 70), "acceptable" (>= 40), or "poor"
References
Kleckner, I.R., et al. (2018). "Simple, transparent, and flexible automated quality assessment procedures for ambulatory electrodermal activity data." IEEE Transactions on Biomedical Engineering, 65(7), 1460-1467. doi:10.1109/TBME.2017.2758643
See also
edaArtifact for artifact detection and correction,
edaFilter for frequency-domain filtering,
edaDecompose for tonic/phasic decomposition