Heart Rate Variability (HRV)

Heart rate variability (HRV) is the variation in the time interval between consecutive heartbeats. HRV is a measure of the autonomic nervous system (ANS) and is often used as a proxy for stress. HRV is also used to assess the risk of cardiovascular disease and sudden cardiac death.

The snippets below form a walkthrough: later snippets may reuse variables from earlier ones. The plots are saved example outputs. For a standalone runnable example, start with the quickstart.

Compute Time-Domain HRV

The hrv.compute_hrv_time function computes time-domain HRV metrics based on the supplied RR-intervals. The RR-intervals can be computed from ECG or PPG signals.

Metric	Description
mean_nn	Mean of NN intervals
sd_nn	Standard deviation of NN intervals
rms_sd	Root mean square of successive differences
sd_sd	Standard deviation of successive differences
cv_nn	Coefficient of variation of NN intervals
cv_sd	Coefficient of variation of successive differences
median_nn	Median of NN intervals
mad_nn	Median absolute deviation of NN intervals
mcv_nn	Median coefficient of variation of NN intervals
iqr_nn	Interquartile range of NN intervals
prc20_nn	20th percentile of NN intervals
prc80_nn	80th percentile of NN intervals
nn50	Number of pairs of successive NN intervals that differ by more than 50 ms
nn20	Number of pairs of successive NN intervals that differ by more than 20 ms
pnn50	Proportion of NN50 divided by total number of NN intervals
pnn20	Proportion of NN20 divided by total number of NN intervals
min_nn	Minimum of NN intervals
max_nn	Maximum of NN intervals

In the following snippet, we generate a synthetic ECG signal, extract its r peaks and RR intervals, and finally compute time-domain HRV metrics.

import numpy as np
import physiokit as pk

tgt_hr = 64 # BPM
fs = 1000 # Hz
signal_length = 8 * fs # 8 seconds

# Generate synthetic ECG signal
ecg, segs, fids = pk.ecg.synthesize(
    signal_length=signal_length,
    sample_rate=fs,
    heart_rate=tgt_hr,
    leads=1
)
ecg = ecg.squeeze()

# Create timestamps
ts = np.arange(0, ecg.size) / fs

# Clean ECG signal
ecg_clean = pk.ecg.clean(
    data=ecg,
    lowcut=2,
    highcut=30,
    order=5,
    sample_rate=fs
)

# Extract R-peaks and RR-intervals
peaks = pk.ecg.find_peaks(ecg_clean, sample_rate=fs)
rri = pk.ecg.compute_rr_intervals(peaks)
mask = pk.ecg.filter_rr_intervals(rri, sample_rate=fs)

# Compute HRV metrics
hrv_td = pk.hrv.compute_hrv_time(
    rr_intervals=rri[mask == 0],
    sample_rate=fs
)

---

Compute Frequency-Domain HRV

The hrv.compute_hrv_frequency function computes frequency-domain HRV metrics based on the supplied RR-intervals. The RR-intervals can be computed from ECG or PPG signals.

Continuing from the previous example, we can also compute frequency-domain HRV metrics.

...

bands = [(0.04, 0.15), (0.15, 0.4), (0.4, 0.5)]
hrv_fd = pk.hrv.compute_hrv_frequency(
    peaks[mask == 0], rri[mask == 0],
    bands=bands,
    sample_rate=fs
)

---

API

Refer to HRV API for more details