Detect QRS complexes in ECG signals

detect_QRS() implements a modified Pan-Tompkins algorithm to detect QRS complexes in ECG signals. The function applies a sequence of processing steps including bandpass filtering, differentiation, squaring, and moving window integration to identify R peaks in the signal.

Usage

detect_QRS(signal, frequency, window_size = 0.15)

Arguments

signal: Numeric vector representing the ECG signal
frequency: Sampling frequency of the signal in Hz
window_size: Width of the integration window in seconds, default is 0.150 seconds

Value

Integer vector containing the sample indices of detected QRS complexes

Details

The Pan-Tompkins algorithm is a widely-used method for QRS detection in ECG signals. This implementation follows these steps:

Bandpass filtering (5-15 Hz) to reduce noise and emphasize QRS complexes
Differentiation to highlight the steep slopes of QRS complexes 3. Squaring to amplify high-frequency components 4. Moving window integration to consider the overall QRS morphology 5. Adaptive thresholding to identify peaks 6. Application of a refractory period to prevent multiple detections of the same QRS complex

The function is designed to work with single-lead ECG signals, typically sampled at 250-1000 Hz.

References

Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, (3), 230-236. doi:10.1109/TBME.1985.325532

Examples

if (FALSE) { # \dontrun{
# Load ECG data
ecg_data <- read_muse(system.file("extdata", "muse-sinus.xml", package = "EGM"))

# Extract lead II signal
signal <- ecg_data$signal$II

# Get sampling frequency from header
freq <- attributes(ecg_data$header)$record_line$frequency

# Detect QRS complexes
qrs_locations <- detect_QRS(signal, freq)

# Plot ECG with detected QRS complexes
plot(signal, type = "l", xlab = "Sample", ylab = "Amplitude")
points(qrs_locations, signal[qrs_locations], col = "red", pch = 19)
} # }