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resp.py
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resp.py
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# -*- coding: utf-8 -*-
"""
biosppy.signals.resp
--------------------
This module provides methods to process Respiration (Resp) signals.
:copyright: (c) 2015-2018 by Instituto de Telecomunicacoes
:license: BSD 3-clause, see LICENSE for more details.
"""
# Imports
# compat
from __future__ import absolute_import, division, print_function
# 3rd party
import numpy as np
# local
from . import tools as st
from .. import plotting, utils
from . import ecg as ecg
from scipy import interpolate
def resp(signal=None, sampling_rate=1000., show=True):
"""Process a raw Respiration signal and extract relevant signal features
using default parameters.
Parameters
----------
signal : array
Raw Respiration signal.
sampling_rate : int, float, optional
Sampling frequency (Hz).
show : bool, optional
If True, show a summary plot.
Returns
-------
ts : array
Signal time axis reference (seconds).
filtered : array
Filtered Respiration signal.
zeros : array
Indices of Respiration zero crossings.
resp_rate_ts : array
Respiration rate time axis reference (seconds).
resp_rate : array
Instantaneous respiration rate (Hz).
"""
# check inputs
if signal is None:
raise TypeError("Please specify an input signal.")
# ensure numpy
signal = np.array(signal)
sampling_rate = float(sampling_rate)
# filter signal
filtered, _, _ = st.filter_signal(signal=signal,
ftype='butter',
band='bandpass',
order=2,
frequency=[0.1, 0.35],
sampling_rate=sampling_rate)
# compute zero crossings
zeros, = st.zero_cross(signal=filtered, detrend=True)
beats = zeros[::2]
if len(beats) < 2:
rate_idx = []
rate = []
else:
# compute respiration rate
rate_idx = beats[1:]
rate = sampling_rate * (1. / np.diff(beats))
# physiological limits
indx = np.nonzero(rate <= 0.35)
rate_idx = rate_idx[indx]
rate = rate[indx]
# smooth with moving average
size = 3
rate, _ = st.smoother(signal=rate,
kernel='boxcar',
size=size,
mirror=True)
# get time vectors
length = len(signal)
T = (length - 1) / sampling_rate
ts = np.linspace(0, T, length, endpoint=True)
ts_rate = ts[rate_idx]
# plot
if show:
plotting.plot_resp(ts=ts,
raw=signal,
filtered=filtered,
zeros=zeros,
resp_rate_ts=ts_rate,
resp_rate=rate,
path=None,
show=True)
# output
args = (ts, filtered, zeros, ts_rate, rate)
names = ('ts', 'filtered', 'zeros', 'resp_rate_ts', 'resp_rate')
return utils.ReturnTuple(args, names)
def ecg_derived_respiration(signal=None, raw_resp = None, sampling_rate=1000., show=True):
"""Process a raw ECG signal and extract the respiration signal and relevant signal features using
default parameters.
Parameters
----------
signal : array
Raw ECG signal.
sampling_rate : int, float, optional
Sampling frequency (Hz).
show : bool, optional
If True, show a summary plot.
Returns
-------
ts : array
Respiration time axis reference (seconds).
signal : array
Respiration derived from ECG signal.
rpeaks : array
R-peak location indices.
zeros : array
Indices of Respiration zero crossings.
resp_rate_ts : array
Respiration rate time axis reference (seconds).
resp_rate : array
Instantaneous respiration rate (Hz).
"""
# check inputs
if signal is None:
raise TypeError("Please specify an input signal.")
# ensure numpy
signal = np.array(signal)
sampling_rate = float(sampling_rate)
# filter signal
order = int(0.3 * sampling_rate)
ecg_filtered, _, _ = st.filter_signal(signal=signal,
ftype='FIR',
band='bandpass',
order=order,
frequency=[3, 45],
sampling_rate=sampling_rate)
# segment
rpeaks, = ecg.hamilton_segmenter(signal=ecg_filtered, sampling_rate=sampling_rate)
# correct R-peak locations
rpeaks, = ecg.correct_rpeaks(signal=ecg_filtered,
rpeaks=rpeaks,
sampling_rate=sampling_rate,
tol=0.05)
#find the amplitude values of the rpeaks, based on the filtered signals and the peaks location
ecg_peaks = [ecg_filtered[e] for e in range(len(ecg_filtered) - 1) if e in rpeaks]
#quadratic interpolation of the peaks
interp = interpolate.interp1d(rpeaks, ecg_peaks, kind='quadratic')
#perform the quadratic interpolation above between the first and last peak.
#create a discrete time between the first and last peaks
#perform the interpolation
resp_signal = interp(np.arange(rpeaks[0],rpeaks[-1]))*[-1]
ts_resp = np.arange((rpeaks[0] / sampling_rate), (rpeaks[-1] / sampling_rate), 1 / sampling_rate)
signal = np.array(resp_signal)
sampling_rate = float(sampling_rate)
# filter signal
derived, _, _ = st.filter_signal(signal=signal,
ftype='butter',
band='bandpass',
order=2,
frequency=[0.1, 0.35],
sampling_rate=sampling_rate)
# compute zero crossings
zeros, = st.zero_cross(signal=derived, detrend=True)
beats = zeros[::2]
if len(beats) < 2:
rate_idx = []
rate = []
else:
# compute respiration rate
rate_idx = beats[1:]
rate = sampling_rate * (1. / np.diff(beats))
# physiological limits
indx = np.nonzero(rate <= 0.35)
rate_idx = rate_idx[indx]
rate = rate[indx]
# smooth with moving average
size = 3
rate, _ = st.smoother(signal=rate,
kernel='boxcar',
size=size,
mirror=True)
# get time vectors
length = len(signal)
T = (length - 1) / sampling_rate
ts = np.linspace(0, T, length, endpoint=True)
ts_rate = ts[rate_idx]
if raw_resp is not None:
raw_resp = raw_resp[rpeaks[0]:rpeaks[-1]]
_,raw_resp,_,ts_raw_rate, raw_rate = resp(raw_resp,show=False)
if show:
plotting.plot_ecg_derived_resp(ts=ts,
raw=raw_resp,
derived=derived,
ecg=ecg_filtered[rpeaks[0]:rpeaks[-1]],
zeros=zeros,
resp_rate_ts=ts_rate,
resp_rate=rate,
raw_rate=raw_rate,
raw_rate_ts=ts_raw_rate,
path=None,
show=True)
else:
if show:
plotting.plot_ecg_derived_resp(ts=ts,
raw=None,
derived=derived,
ecg=ecg_filtered[rpeaks[0]:rpeaks[-1]],
zeros=zeros,
resp_rate_ts=ts_rate,
resp_rate=rate,
raw_rate=None,
raw_rate_ts=None,
path=None,
show=True)
# plot
# output
args = (ts_resp, raw_resp, derived, zeros, ts_rate, rate)
names = ('ts', 'real','filtered', 'zeros', 'resp_rate_ts', 'resp_rate')
return utils.ReturnTuple(args, names)