Title: Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases,
Abstract: $\textbf{Objective}$: To assess the validity of an automatic EEG arousal
detection algorithm using large patient samples and different heterogeneous
databases
$\textbf{Methods}$: Automatic scorings were confronted with results from
human expert scorers on a total of 2768 full-night PSG recordings obtained from
two different databases. Of them, 472 recordings were obtained during clinical
routine at our sleep center and were subdivided into two subgroups of 220
(HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by
the clinical expert during the visual review (semi-automatic or purely manual,
respectively). In addition, 2296 recordings from the public SHHS-2 database
were evaluated against the respective manual expert scorings.
$\textbf{Results}$: Event-by-event epoch-based validation resulted in an
overall Cohen kappa agreement K = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573
(SHHS-2). Estimated inter-scorer variability on the datasets was, respectively,
K = 0.594, 0.561 and 0.543. Analyses of the corresponding Arousal Index scores
showed associated automatic-human repeatability indices ranging in 0.693-0.771
(HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 (SHHS-2).
$\textbf{Conclusions}$: Large-scale validation of our automatic EEG arousal
detector on different databases has shown robust performance and good
generalization results comparable to the expected levels of human agreement.
Special emphasis has been put on allowing reproducibility of the results and
implementation of our method has been made accessible online as open source
code