Automated Cardiac Data Processing via ACF, GA & Tracking Index.

CardiacDP

CardiacDP (Cardiac Data Processing) can automatically read and collate heart rate data, and compute average heart rate per analysis interval (sequence). The analysis employs the autocorrelation function (ACF) with a genetic algorithm framework to identify periods of repeating waveforms within each sequence. These candidate heart rates of sub-sequences are then evaluated either by the autocorrelation value or a tracking index (TI), and finally weighted as the final output.

Package structure

Installation

install.packages("CardiacDP")

# Or from GitHub
devtools::install_github("Vicellken/CardiacDP")

# Or the source package from GitHub:
# Vector of package names
packages <- c("data.table", "doParallel", "dplyr", "foreach", "ggplot2", "purrr", "RColorBrewer", "stringr")

lapply(packages, function(pkg) {
  if (!require(pkg, character.only = TRUE)) {
    install.packages(pkg)
  }
})

# Install the source package
install.packages("CardiacDP_0.4.2.tar.gz", repos = NULL, type = "source")

Example

This is an example showing the complete analysis pipeline:

library(CardiacDP)

zip_path <- "~/rawfiles.zip" # PATH_TO_ZIP_FILE

# csv_path <- "" # PATH_TO_COLLATED_CSV

# run computeHR() for average heart rate analysis (no files are written by default)
output <- computeHR(
  file_path = csv_path, # collatedata() will automatically run if zip_path is directly provided here
  save_outputs = TRUE,
  output_dir = NULL, # PATH_TO_STORE_OUTPUTS
  verbose = TRUE)

The user can access the output by the channel name. finalsubseq is a list showing the positions and durations of the final periodic sub-sequences determined for each sequence. They are presented as data tables (s = start index of the sub-sequence; e = end index of the sub-sequence; p = which of the initial population the sub-sequence is derived from; and f = duration of the sub-sequence), the rows of which represent separate final sub-sequences.

# positions (in indices) and durations of the final sub-sequences
# NOTE: the results are not included here for simplicity. For more details please refer to Supplementary Information S4 of the publication / User guideline.pdf

# output[["finalsubseq"]][["Channel A"]]

The corresponding candidate heart rates per sub-sequences can be found in candidateHR (ACF = autocorrelation value; lag = time lag; and hr = heart rate).

# candidate heart rates of the final sub-sequences
# NOTE: the results are not included here for simplicity. For more details please refer to Supplementary Information S4 of the publication / User guideline.pdf

# output[["candidateHR"]][["Channel A"]]

The final results after evaluating the candidate heart rates, checking for resolution and weighting for durations can eventually be obtained from results_ACF and results_TI. These results referred to evaluating the candidate heart rates by merely autocorrelation values (i.e. the “ACF + GA” approach in the publication) or the tracking index (i.e. the “ACF + GA +TI” approach in the publication) respectively. Each of them consists of 1) the details of the sub-sequences (subseqHR); 2) the weighted heart rate per sequence (weightedHR); and 3) a plot of weighted heart rate against time (plot).

## results obtained from evaluating the candidate heart rates by autocorrelation values
# output[["results_ACF"]][["Channel A"]]

# results obtained from evaluating the candidate heart rates by the tracking index
# output[["results_TI"]][["Channel A"]]

Interpret results

Automatically saved files

By default, computeHR() does not write any files. If you want CSV/PNG outputs, set save_outputs = TRUE and optionally provide output_dir (if output_dir is NULL, it defaults to tempdir()):

csv_path <- system.file("extdata", "example.csv", package = "CardiacDP")
output <- computeHR(
  file_path = csv_path,
  save_outputs = TRUE,
  output_dir = tempdir()
)

CSV Files

For each channel and method (ACF and TI), the following CSV files are saved:

• {Channel}_ACF_subseqHR.csv - Details of sub-sequences with heart rates (ACF method)
• {Channel}_ACF_weightedHR.csv - Weighted heart rates per analysis interval (ACF method)
• {Channel}_TI_subseqHR.csv - Details of sub-sequences with heart rates (TI method)
• {Channel}_TI_weightedHR.csv - Weighted heart rates per analysis interval (TI method)

Note: Channel names with spaces (e.g., “Channel A”) are converted to underscores (e.g., “Channel_A”) in filenames.

PNG Files

For each channel and method, heart rate plots are saved:

• {Channel}_ACF_plot.png - Heart rate plot (ACF method)
• {Channel}_TI_plot.png - Heart rate plot (TI method)

Column descriptions

weightedHR CSV files contain:

• ix - Sequence index (1, 2, 3, …)
• Time_min - Time in minutes (midpoint of analysis interval = (ix - 0.5) × analysis_interval)
• wACF - Weighted autocorrelation value
• whr - Weighted heart rate in beats per minute (can be NA)

subseqHR CSV files contain:

• ix - Sequence index
• win - Sub-sequence window number within the sequence
• s, e, p, f - Sub-sequence parameters (start, end, population, fitness)
• ACF - Autocorrelation value
• lag - Time lag
• hr - Heart rate in beats per minute (can be NA)
• res - Resolution used

Reproducing plots from saved CSV files

The saved CSV files contain all the data needed to reproduce the heart rate plots. Use the weightedHR CSV files for plotting:

library(ggplot2)
library(RColorBrewer)
library(data.table)

# Read the saved weighted heart rate data (example; assumes you ran computeHR(save_outputs=TRUE))
channel <- "Channel A"
data <- fread(paste0(gsub(" ","_",channel),"_TI_weightedHR.csv"))

# Create the plot (matching the package output)
palette <- brewer.pal(n = 11, name = "RdYlBu")
names(palette) <- seq(0, 10, 1) / 10

# analysis interval (by default 1 min)
an_in <- 1

ggplot() +
  geom_point(
    data = data,
    aes(x = ix * an_in, y = whr, fill = factor(floor(wACF * 10) / 10)),
    colour = "black", shape = 21, size = 4, alpha = 0.8
  ) +
  scale_fill_manual(
    name = "ACF", values = palette, na.value = NA,
    na.translate = FALSE, guide = "legend"
  ) +
  scale_y_continuous(
    name = "Heart rate (bpm)",
    breaks = seq(0, ceiling(max(data$whr, na.rm = TRUE) / 50) * 50, 50),
    limits = c(0, ceiling(max(data$whr, na.rm = TRUE) / 50) * 50),
    expand = c(0, 0)
  ) +
  scale_x_continuous(
    name = "Time (min)",
    breaks = seq(0, ceiling(max(data$Time_min, na.rm = TRUE) / 30) * 30, 30),
    limits = c(0, ceiling(max(data$Time_min, na.rm = TRUE) / 30) * 30),
    expand = c(0, 0)
  ) +
  theme_linedraw() +
  theme(
    text = element_text(size = 12, colour = "black"),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(fill = NA, colour = "black", linewidth = 1)
  )

Key columns for plotting:

• X-axis:ix * an_in - Time in minutes
• Y-axis:whr - Weighted heart rate (bpm)
• Fill color:wACF - Weighted autocorrelation value (colored by discrete bins)