import numpy as np
def compute_hr(sig_len, qrs_inds, fs):
"""
Compute instantaneous heart rate from peak indices.
Parameters
----------
sig_len : int
The length of the corresponding signal.
qrs_inds : ndarray
The QRS index locations.
fs : int, float
The corresponding signal's sampling frequency.
Returns
-------
heart_rate : ndarray
An array of the instantaneous heart rate, with the length of the
corresponding signal. Contains numpy.nan where heart rate could
not be computed.
"""
heart_rate = np.full(sig_len, np.nan, dtype='float32')
if len(qrs_inds) < 2:
return heart_rate
for i in range(0, len(qrs_inds)-2):
a = qrs_inds[i]
b = qrs_inds[i+1]
c = qrs_inds[i+2]
rr = (b-a) * (1.0 / fs) * 1000
hr = 60000.0 / rr
heart_rate[b+1:c+1] = hr
heart_rate[qrs_inds[-1]:] = heart_rate[qrs_inds[-1]]
return heart_rate
def calc_rr(qrs_locs, fs=None, min_rr=None, max_rr=None, qrs_units='samples',
rr_units='samples'):
"""
Compute R-R intervals from QRS indices by extracting the time
differences.
Parameters
----------
qrs_locs : ndarray
1d array of QRS locations.
fs : float, optional
Sampling frequency of the original signal. Needed if
`qrs_units` does not match `rr_units`.
min_rr : float, optional
The minimum allowed R-R interval. Values below this are excluded
from the returned R-R intervals. Units are in `rr_units`.
max_rr : float, optional
The maximum allowed R-R interval. Values above this are excluded
from the returned R-R intervals. Units are in `rr_units`.
qrs_units : str, optional
The time unit of `qrs_locs`. Must be one of: 'samples',
'seconds'.
rr_units : str, optional
The desired time unit of the returned R-R intervals in. Must be
one of: 'samples', 'seconds'.
Returns
-------
rr : ndarray
Array of R-R intervals.
"""
rr = np.diff(qrs_locs)
# Empty input qrs_locs
if not len(rr):
return rr
# Convert to desired output rr units if needed
if qrs_units == 'samples' and rr_units == 'seconds':
rr = rr / fs
elif qrs_units == 'seconds' and rr_units == 'samples':
rr = rr * fs
# Apply R-R interval filters
if min_rr is not None:
rr = rr[rr > min_rr]
if max_rr is not None:
rr = rr[rr < max_rr]
return rr
def calc_mean_hr(rr, fs=None, min_rr=None, max_rr=None, rr_units='samples'):
"""
Compute mean heart rate in beats per minute, from a set of R-R
intervals. Returns 0 if rr is empty.
Parameters
----------
rr : ndarray
Array of R-R intervals.
fs : int, float
The corresponding signal's sampling frequency. Required if
'input_time_units' == 'samples'.
min_rr : float, optional
The minimum allowed R-R interval. Values below this are excluded
when calculating the heart rate. Units are in `rr_units`.
max_rr : float, optional
The maximum allowed R-R interval. Values above this are excluded
when calculating the heart rate. Units are in `rr_units`.
rr_units : str, optional
The time units of the input R-R intervals. Must be one of:
'samples', 'seconds'.
Returns
-------
mean_hr : float
The mean heart rate in beats per minute.
"""
if not len(rr):
return 0
if min_rr is not None:
rr = rr[rr > min_rr]
if max_rr is not None:
rr = rr[rr < max_rr]
mean_rr = np.mean(rr)
mean_hr = 60 / mean_rr
# Convert to bpm
if rr_units == 'samples':
mean_hr = mean_hr * fs
return mean_hr