Package 'CircaCP'

Example Actigraphy Dataset

Description

A single-subject actigraphy dataset from the NHANES 2013–2014 study, provided as an example for demonstrating the CircaCP workflow.

Usage

actigraphy

Format

A data frame with one row per recorded epoch and the following variables:

Date

Date of recording (synthetic or reconstructed from NHANES information).

Time

Time of day corresponding to each observation.

Lux

Light intensity.

SDLux

Short-term variability of light intensity.

MIMS

Activity magnitude calculated using the MIMS algorithm.

X

Raw or calibrated X-axis signal from the accelerometer.

Y

Raw or calibrated Y-axis signal from the accelerometer.

Z

Raw or calibrated Z-axis signal from the accelerometer.

Source

National Health and Nutrition Examination Survey (NHANES) 2013–2014.

Examples

data(actigraphy)
head(actigraphy)

---

Detect a single change point (parametric methods)

Description

Detect a single change point (parametric methods)

Usage

cp_detect(M, dist)

Arguments

M	minute-level Activity vector;
dist	Actigraphy data distribution family, including Gaussian, Gamma, ZAG (Zero-Augmented Gamma), Poisson, Exponential

Value

the location of the single change point

See Also

sleep_detection()

---

Nonparametric circadian metrics (RA, IS, IV, L5, M10)

Description

Computes five nonparametric metrics from minute-level activity: Relative Amplitude (RA), Interdaily Stability (IS), Intradaily Variability (IV), the least-active 5-hour block (L5), and the most-active 10-hour block (M10).

Usage

extract_nonparametric_metrics(df, L_window = 5 * 60, M_window = 10 * 60)

Arguments

df	cleaned data frame containing the activity varialbe at 1-minute epoch
L_window	Integer window length (minutes) for L5 (default 300).
M_window	Integer window length (minutes) for M10 (default 600).

Value

A list with components:

RA

Relative amplitude, $(M10 - L5) / (M10 + L5)$ when denominator $> 0$.

IS

Interdaily stability (0..1).

IV

Intradaily variability ($\ge 0$).

L5_mean

Mean activity in the lowest 5-hour block of the 24 h profile.

L5_start_min

Minute-of-day (0~1439) at which L5 starts.

M10_mean

Mean activity in the highest 10-hour block of the 24 h profile.

M10_start_min

Minute-of-day (0~1439) at which M10 starts.

profile_24h

Length-1440 vector of minute-of-day means.

See Also

screen_wear(), sleep_detection(), extract_sleep_metrics

---

Extract metrics related to sleep and circadian rhythm after using CircaCP algorithm

Description

From minute-level data with sleep/wake labels (label.sw 1 = sleep, 0 = wake), extracts sleep/wake onsets, episode durations, circular SDs of onset times, Sleep Regularity Index (SRI), cosinor parameters, day/night variance ratio, and nonparametric metrics (RA, IS, IV, L5, M10). Returns one row per episode with scalar metrics repeated per row (tidy format).

Usage

extract_sleep_metrics(df, min_sleep_episode_min = 180L)

Arguments

df	data.frame with columns id, Date, Time, Activity, label.sw.
min_sleep_episode_min	Minimum duration (minutes) to treat as a main sleep episode.

Value

A data.frame with columns including:

id

Subject ID obtained from the stem of filename.

period_type

sleep or wake

timestamp

datetime of sleep onset time and wake onset time

clock_min

timestamps presented as minutes of the day since midnight

duration_hours

sleep duration or wake duration

SleepTimeSD_hours

Standard devation of sleep onset time (calculated by circular statistics)

WakeTimeSD_hours

Standard devation of sleep onset time (calculated by circular statistics)

SRI

Sleep regularity index

Mesor, Amplitude, Acrophase

parameters obtained from the cosinor model

RA, IS, IV, L5_mean, L5_start_min, M10_mean, M10_start_min

nonparametrics actigraphy metrics

See Also

sleep_detection(), sleep_cos(), extract_nonparametric_metrics()

---

Extract contiguous sleep or wake periods from a labeled minute series

Description

Splits a minute-level, labeled time series into contiguous episodes of a target state (sleep or wake) and returns the activity vectors for those episodes whose length is at least min_len minutes.

Usage

extract_sw_period(labeled_df, target_state = 1, min_len = 30)

Arguments

labeled_df	A data.frame containing at least: Activity — numeric minute-level activity. label.sw — binary sleep/wake label per minute (1 = sleep, 0 = wake). NA values are treated as breaks between episodes.
target_state	Integer 0 or 1. Use 0 to extract sleep episodes, 1 to extract wake episodes. Default is 1.
min_len	Integer minimum episode length in minutes (i.e., number of consecutive samples) required to keep an episode. Default is 30.

Details

The function uses run-length encoding over label.sw to identify contiguous episodes. Any NA in label.sw is converted to a sentinel and treated as a hard break (i.e., episodes do not cross NA gaps). Length filtering is applied on the number of minutes (rows) per episode.

Value

A list of numeric vectors. Each element is the Activity values for one qualifying episode of the requested state. If no episode qualifies, returns an empty list (list()).

See Also

sleep_detection()

#' @export

---

Import actigraphy with header stripping and harmonized Activity

Description

Reads a CSV/TSV, automatically strips any leading header lines, selects user specified Date and Time columns, and harmonizes one chosen activity column to the canonical name Activity. Adds an id (defaults to the file stem if not supplied).

Usage

import_acti_file(
  file,
  date_col,
  time_col,
  activity_cols,
  id = NULL,
  keep_extra = FALSE,
  drop_original_activity_cols = TRUE
)

Arguments

file	Character path to the actigraphy file.
date_col, time_col	Character names of the date and time columns in file.
activity_cols	Character vector of candidate activity columns; the first that exists will be used and renamed to Activity.
id	Optional subject id (character). Defaults to the filename stem.
keep_extra	Logical; if FALSE keeps only ⁠id, Date, Time, Activity⁠, otherwise preserves extra columns present in the file.
drop_original_activity_cols	Logical; if TRUE, drop the original activity

Details

Internally uses data.table::fread() with automatic header detection (skips lines before the first row that looks like a header). No time zone conversion is performed.

Value

A data.frame with at least columns id, Date, Time, Activity.

See Also

screen_wear()

---

Screen wear and extract the longest valid minute-level segment

Description

Determines the native epoch from the first two time stamps, decimates to 1-minute resolution (if needed), checks total minutes $\ge$ 1440*min_days, and within that finds the longest contiguous segment where runs of consecutive zeros do not exceed max_zero_run minutes.

Usage

screen_wear(
  df,
  min_days = 5L,
  max_zero_run = 120L,
  date_col = "Date",
  time_col = "Time",
  activity_col = "Activity"
)

Arguments

df	data.frame with columns Date, Time, Activity in time order.
min_days	Integer minimum number of whole days required (default 5).
max_zero_run	Integer maximum allowed length (minutes) of a run of zeros.
date_col	Name of the date column.
time_col	Name of the time column.
activity_col	Name of the activity column used to determine wear/non-wear.

Details

Decimation rule: 15 s $\rightarrow$ factor 4; 30 s $\rightarrow$ factor 2; 60 s $\rightarrow$ factor 1. The zero-run criterion is applied on the 1-minute Activity series.

Value

A list with elements:

status

"ok" if a qualifying segment is found, otherwise a message.

epoch_inferred

Detected original epoch in seconds (15, 30, 60).

out_idx

Indices of the kept segment in the input.

clean_df

Minute-level data.frame of the selected segment (if ok).

See Also

import_acti_file(), sleep_cos(), sleep_detection()

---

Estimates circadian cycle by cosinor fit of minute-level activity (period = 1440)

Description

Fits $C(t) = m + a \cos\{2\pi (t - \phi)/P\}$ with period $P = 1440$ minutes to a rescaled activity series. Returns fitted curve, binary curve after thresholding, and canonicalized parameters (non-negative amplitude). MESOR (mesor) The baseline or “midline” level around which the rhythm oscillates. Units = same as actigraphy your data Roughly the average activity across the cycle.

Usage

sleep_cos(clean_df, thr = 0.2)

Arguments

clean_df	cleaned dataframe with cleaned_df$Activity.
thr	Dichotomization threshold for fitted curve.

Details

Amplitude (amp) Half the peak-to-trough swing of the fitted rhythm. Units is the same as actigraphy data. Larger amplitude → stronger rhythmicity (bigger day–night contrast). Negative amp will be flipped, and P/2 will be added to phase. Acrophase (phase) The timing of the peak of the fitted cosine. Units is the epoch units of the data (e.g. minutes). P=1440, interpret phase as minutes-of-day relative to your x origin.

Value

A list with elements:

fitted

fitted cosine curve.

label.cos

dichotimized cosine curve

cos_para

c(Mesor, Amplitude, Acrophase).

rmse

Root mean squared error between fitted cosine curve and data.

See Also

screen_wear(), sleep_detection(), extract_sleep_metrics()

---

Estimate precise sleep_wake cycles using CircaCP algorithm

Description

It first uses a 24 h cosinor fit (via sleep_cos()) and a chosen thresholding rule to label each minute as sleep (label.sw = 1) or wake (label.sw = 0). Reference: Shanshan Chen, and Xinxin Sun.Validating CircaCP: a generic sleep–wake cycle detection algorithm for unlabelled actigraphy data. Royal Society Open Science 11, no. 5 (2024): 231468.

Usage

sleep_detection(clean_df, thr = 0.2, dist = "ZAG")

Arguments

clean_df	Minute-level data.frame with at least Activity. Additional columns are preserved.
thr	Numeric threshold in [0, 1] applied to a rescaled cosinor fit.
dist	Character method key (e.g., "ZAG"); interpreted by your rule set.

Value

a data.frame augmenting the input df including the following additional variables:

cosinor

fitted cosine curve

label.cos

circadian cycle estimated by dichotimized cosine curve

label.sw

sleep-wake cycle estimated by CircaCP

Activity_norm

range-normalized activity levels

See Also

screen_wear(), sleep_cos(),cp_detect(), extract_sleep_metrics()

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