Detect secondary sleep periods (naps) using the Crespo nap algorithm

Faithful port of the Python nap_wrapper: runs the full CSPD model in nap mode (detect_naps = TRUE) on the currently-awake epochs (state == 0) and merges the detected naps into the sleep state. Must be run after detect_sleep_crespo().

Usage

detect_naps_crespo(x, epoch_h = NULL, params = .cspd_nap_params())

Arguments

x: A tibble as returned by detect_sleep_crespo(), containing columns datetime, activity, and state. Nap detection runs on the wake (state == 0) subset only, mirroring the Python nap_bool mask.
epoch_h: numeric(1). Number of epochs per hour. If NULL (default), derived from the wake-subsequence epoch duration as 3600 / duration.
params: CSPD nap configuration list (default .cspd_nap_params()), the port of nap_wrapper's parameter set.

Value

The input tibble x with state and sleep columns updated. Nap epochs become state == 1 and sleep == 1; off-wrist (state == 4) and existing main-sleep epochs are preserved.

Details

Nap detection uses a nap-mode MSP (a high zero-proportion combined with a low adaptive-median activity, .crespo_nap_msp()) followed by the same bed-time / get-up-time refiners as the main sleep detection, with the nap parameter set (.cspd_nap_params()) and nap-specific minimum-length post-processing. Detected naps are written as state == 1 (merged into "sleep"), matching nap_wrapper, which assigns state[wake] = 1 - refined_output (i.e. naps are not a distinct state).

References

Crespo, C., Aboy, M., Fernández, J. R., & Mojón, A. (2012). Automatic identification of activity-rest periods based on actigraphy. Journal of Medical and Biological Engineering, 32(4), 249–256. doi:10.5405/jmbe.1033

Examples

if (FALSE) { # \dontrun{
rec  <- read_acttrust("recordings/P001.txt")
prep <- prepare_actigraphy(rec)
prep <- detect_offwrist_bimodal(prep)
prep <- detect_sleep_crespo(prep)
prep <- detect_naps_crespo(prep)
} # }