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ECG Analysis Functions for PhysioExperiment Objects

PhysioECG provides electrocardiography (ECG) analysis functions for the PhysioExperiment ecosystem. With 18 exported functions, it delivers a complete ECG analysis pipeline – from R-peak detection using an adaptive Pan-Tompkins algorithm, through RR interval computation and ectopic beat correction, to comprehensive heart rate variability (HRV) analysis across time-domain, frequency-domain, and nonlinear methods. It also includes ECG morphology analysis (PQRST waveform delineation, clinical interval measurement) and signal quality assessment.

Installation

You can install PhysioECG from r-universe:

install.packages("PhysioECG",
  repos = c("https://x-biosignal.r-universe.dev", "https://cloud.r-project.org"))

Or install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("x-biosignal/PhysioECG")

Quick Start 

library(PhysioECG)

# Generate a simulated ECG signal (10 seconds at 500 Hz, 72 bpm)
pe <- make_ecg(n_time = 5000, sr = 500, heart_rate = 72)

# Detect R-peaks using the adaptive Pan-Tompkins algorithm
peaks <- ecgDetectRpeaks(pe)
head(peaks)
#>   channel sample time_sec amplitude
#> 1       1    347    0.692      1.50
#> 2       1    764    1.526      1.50
#> ...

# Compute RR intervals
rr <- ecgRRintervals(pe, peaks)

# Time-domain HRV metrics
hrv_time <- ecgHRVtime(rr)
hrv_time
#>   channel mean_rr   sdnn rmssd pnn50 mean_hr
#> 1       1  833.33   1.23  0.87     0   72.00

# Frequency-domain HRV analysis (Welch PSD)
hrv_freq <- ecgHRVfreq(rr, method = "welch")

# Nonlinear HRV (Poincare, Sample Entropy, DFA)
hrv_nl <- ecgHRVnonlinear(rr)

# ECG morphology: delineate P, QRS, T waves
delin <- ecgDelineate(pe, peaks)
intervals <- ecgIntervals(delin, sr = 500)
head(intervals)
#>   channel beat  pr_ms  qt_ms  qtc_ms qrs_ms  rr_ms
#> 1       1    1  126.0  360.0   394.1   80.0  833.3

Features

R-Peak Detection

Adaptive dual-threshold Pan-Tompkins detector with automatic inverted-signal handling:

  • ecgDetectRpeaks() – detect R-peaks using bandpass filtering (5–15 Hz), differentiation, squaring, moving-window integration, and adaptive thresholding

RR Interval Analysis

RR interval computation and ectopic beat correction:

HRV Time-Domain Analysis

Standard time-domain HRV metrics per ESC/NASPE Task Force guidelines:

  • ecgHRVtime() – SDNN, RMSSD, pNN50, mean RR, mean heart rate

HRV Frequency-Domain Analysis

Power spectral density estimation with standard frequency band integration:

  • ecgHRVfreq() – VLF (0.003–0.04 Hz), LF (0.04–0.15 Hz), HF (0.15–0.4 Hz) power, LF/HF ratio, total power
  • Supports Welch’s method (resampled FFT) and Lomb-Scargle periodogram

HRV Nonlinear Analysis

Nonlinear dynamics and complexity measures:

  • ecgHRVnonlinear() – convenience wrapper combining all nonlinear metrics
  • ecgHRVpoincare() – Poincare plot descriptors (SD1, SD2, SD1/SD2 ratio)
  • ecgSampleEntropy() – sample entropy for signal complexity/regularity
  • ecgDFA() – detrended fluctuation analysis (alpha1 short-range, alpha2 long-range)

ECG Morphology Analysis

Waveform delineation and clinical interval measurement:

  • ecgDelineate() – identify QRS complex boundaries, P-wave and T-wave peaks, and T-wave end for each beat
  • ecgIntervals() – compute PR interval, QRS duration, QT interval, and QTc (Bazett correction)

Signal Quality Assessment

Signal quality evaluation and baseline correction:

  • ecgSignalQuality() – per-channel SNR, baseline wander, saturation ratio, and composite quality score
  • ecgBaselineCorrect() – remove baseline wander using highpass moving-average or running-median subtraction

Simulated ECG Data

Synthetic ECG generators for testing and demonstration:

  • make_ecg() – regular ECG with Gaussian-shaped R-peaks at fixed heart rate
  • make_ecg_pqrst() – realistic PQRST morphology with known fiducial points for validation
  • make_ecg_irregular() – ECG with simulated ectopic (premature) beats and compensatory pauses
  • make_ecg_noisy() – ECG contaminated with baseline wander, powerline interference, and broadband noise

Dependencies

  • R (>= 4.2)
  • PhysioCore
  • SummarizedExperiment
  • S4Vectors
  • stats

PhysioExperiment Ecosystem

PhysioECG is the ECG analysis module of the PhysioExperiment ecosystem, a suite of R packages for multi-modal physiological signal analysis:

Package Description
PhysioCore Core data structures and accessors
PhysioIO File I/O (EDF, HDF5, BIDS, CSV, MAT)
PhysioPreprocess Preprocessing (filters, ICA, resampling)
PhysioAnalysis Analysis and visualization
PhysioECG ECG analysis and HRV
PhysioEDA Electrodermal activity analysis

Visit the r-universe page to browse all available packages.

References

  • Pan, J. & Tompkins, W.J. (1985). “A real-time QRS detection algorithm.” IEEE Transactions on Biomedical Engineering, 32(3), 230–236.
  • Task Force of the ESC and NASPE (1996). “Heart rate variability: Standards of measurement, physiological interpretation and clinical use.” Circulation, 93(5), 1043–1065.
  • Shaffer, F. & Ginsberg, J.P. (2017). “An overview of heart rate variability metrics and norms.” Frontiers in Public Health, 5, 258.
  • Richman, J.S. & Moorman, J.R. (2000). “Physiological time-series analysis using approximate entropy and sample entropy.” American Journal of Physiology, 278(6), H2039–H2049.
  • Peng, C.-K., et al. (1994). “Mosaic organization of DNA nucleotides.” Physical Review E, 49(2), 1685–1689.

License

MIT License. See LICENSE for details.

Author

Yusuke Matsui

Governance & support

Part of the Physio ecosystem. Community and policy documents live in the umbrella repository: