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API Reference

This document describes the main functions available in the pySKNA library.

extract_SKNA(...)

Extracts the integrated Skin Nerve Activity (iSKNA) using bandpass filtering and smoothing from a 1D ECG-derived signal.

Parameters:

  • input_ary (np.ndarray): 1D NumPy array of type float32, typically an ECG signal.
    Note: Many ECG devices apply built-in filtering, which may attenuate or remove SKNA components.
  • fs (int): Sampling frequency in Hz.
  • f_l (float, optional): Lower cutoff frequency for bandpass filtering (default: 500 Hz).
  • f_h (float, optional): Upper cutoff frequency for bandpass filtering (default: 1000 Hz), # (None for highpass).
  • smoothing_win_len (float, optional): Length of the moving average smoothing window in seconds (default: 0.1).

Returns:

A dictionary containing:

  • FilteredEKG (np.ndarray): The bandpass-filtered ECG signal.
  • iSKNA (np.ndarray): The integrated SKNA signal after rectification and smoothing.

Reference:

Kusayama, T., Wong, J., Liu, X., He, W., Doytchinova, A., Robinson, E. A., ... & Chen, P. S. (2020).
Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG).
Nature Protocols, 15(5), 1853–1877.

extract_TVSKNA(...)

Extracts the time-varying Skin Nerve Activity (TVSKNA) using Variable Frequency Complex Demodulation (VFCDM) from a 1D ECG-derived signal.

  • VFCDM reference:

    Wang, H., Siu, K., Ju, K., & Chon, K. H. (2006). A high resolution approach to estimating time-frequency spectra and their amplitudes. Annals of biomedical engineering, 34(2), 326-338.

Parameters:

  • input_ary (np.ndarray): 1D NumPy array of type float32, typically an ECG signal. Note: Many ECG devices apply built-in filtering, which may attenuate or remove SKNA components.
  • fs (int): Sampling frequency in Hz. Must be one of: 500, 1000, or 4000.
  • smoothing_win_len (float, optional): Length of the smoothing window in seconds (default: 0.1).
  • thread_n (int, optional): Number of parallel threads to use (default: 12). Acceptable range: 1–12.
  • verbose (int, optional): Verbosity level (default: 1). Set to 1 to display progress messages; 0 for silent mode.

Returns:

A dictionary with the following keys, depending on the sampling frequency:

For 1000 Hz and 4000 Hz sampling:

Multiple frequency bands are extracted. The dictionary includes:

  • TVSKNA1_signal (np.ndarray): Time-varying SKNA signal for frequency band 1.

  • TVSKNA1_freq (str): Label for frequency band 1 (e.g., "500–1000 Hz").

  • TVSKNA2_signal (np.ndarray): Time-varying SKNA signal for frequency band 2.

  • TVSKNA2_freq (str): Label for frequency band 2.

  • TVSKNA3_signal (np.ndarray): Time-varying SKNA signal for frequency band 3.

  • TVSKNA3_freq (str): Label for frequency band 3.

For 500 Hz sampling:

Only a single frequency band is extracted. The dictionary includes:

  • TVSKNA_signal (np.ndarray): Time-varying SKNA signal.
  • TVSKNA_freq (str): Frequency band label (e.g., "150–250 Hz").

References:

  • For 4 kHz sampling:

    Kong, Y., Baghestani, F., D'Angelo, W., Chen, I. P., & Chon, K. H. (2025).
    A New Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis.
    IEEE Transactions on Affective Computing.

  • For 500 and 1000 Hz sampling:

    Kong, Y., Baghestani, F., D'Angelo, W., Chen, I. P., & Chon, K. H. (2025).
    A New Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis.
    IEEE Transactions on Affective Computing.

    Kong, Y., Baghestani, F., Chen, I. P., & Chon, K. H. (2025).
    Feasibility of Extracting Skin Nerve Activity from Electrocardiogram Recorded at a Low Sampling Frequency.
    arXiv preprint, arXiv:2508.00494. https://doi.org/10.48550/arXiv.2508.00494