find_local_maxima: Find Local Maxima in Glucose Time Series

In cgmguru: Advanced Continuous Glucose Monitoring Analysis with High-Performance C++ Backend

Find Local Maxima in Glucose Time Series

Description

Identifies local maxima (peaks) in glucose concentration time series data. Uses a difference-based algorithm to detect peaks where glucose values increase or remain constant for two consecutive points before the peak point, and decrease or remain constant for two consecutive points after the peak point.

Usage

find_local_maxima(df)

Arguments

df	A dataframe containing continuous glucose monitoring (CGM) data. Must include columns: id: Subject identifier (string or factor) time: Time of measurement (POSIXct) gl: Glucose value (integer or numeric, mg/dL)

Value

A list containing:

• local_maxima_vector: Tibble with R-based (1-indexed) row numbers of local maxima (local_maxima). The corresponding occurrence time is df$time[local_maxima] and glucose is df$gl[local_maxima].

• merged_results: Tibble with local maxima details (id, time, gl)

See Also

grid, mod_grid, find_new_maxima

Other GRID pipeline: detect_between_maxima(), find_max_after_hours(), find_max_before_hours(), find_min_after_hours(), find_min_before_hours(), find_new_maxima(), grid(), maxima_grid(), mod_grid(), start_finder(), transform_df()

Examples

# Load sample data
library(iglu)
data(example_data_5_subject)
data(example_data_hall)

# Find local maxima
maxima_result <- find_local_maxima(example_data_5_subject)
print(paste("Found", nrow(maxima_result$local_maxima_vector), "local maxima"))

# Find maxima on larger dataset
large_maxima <- find_local_maxima(example_data_hall)
print(paste("Found", nrow(large_maxima$local_maxima_vector), "local maxima in larger dataset"))

# View first few maxima
head(maxima_result$local_maxima_vector)

# View merged results
head(maxima_result$merged_results)

cgmguru documentation built on Nov. 6, 2025, 1:07 a.m.