def _computeSignal(self, signal):
obj = {}
parsCount = 0
HZ = len(signal) / 120
HZstep = 1000 / HZ
stats = time_domain(signal).stats
peaks = getPeaks(signal)
peaks_diff = tools.nni_diff(peaks)
peaks_diff_ms = peaks_diff * HZstep
M = np.mean(peaks_diff_ms)
sdnn_ms = np.std(peaks_diff_ms)
cv = (sdnn_ms / M) * 100
n, bins, _ = plt.hist(peaks_diff_ms)
M0 = bins[np.argmax(n)]
AM0 = (sum(n) / 100) * max(n)
vr = np.var(bins)
IN = AM0 / (2 * M0 * vr)
parsCount += abs(self._firstParam(M))
parsCount += abs(self._secoundParam(sdnn_ms, vr / M, cv))
parsCount += abs(self._thirdParam(vr, AM0, IN))
parsCount += abs(self._fourthParam(cv))
parsCount += abs(
self._fifthParam(stats['VLF'], stats['LF'], stats['HF']))
return parsCount
def nnXX(nni=None, rpeaks=None, threshold=None):
"""Find number of NN interval differences greater than a specified threshold and ratio between number of intervals
> threshold and total number of NN interval differences.
References: [Electrophysiology1996], [Ewing1984]
Docs: https://pyhrv.readthedocs.io/en/latest/_pages/api/time.html#nnxx-nnxx
Parameters
----------
nni : array
NN intervals in [ms] or [s].
rpeaks : array
R-peak times in [ms] or [s].
threshold : int
Threshold for nnXX values in [ms].
Returns (biosppy.utils.ReturnTuple Object)
------------------------------------------
[key : format]
Description.
nnXX: int
Number of NN interval differences greater than the specified threshold [-].
pnnXX : float
Ratio between nnXX and total number of NN interval differences [-].
Notes
-----
.. Only one type of input data is required
.. If both 'nni' and 'rpeaks' are provided, 'nni' will be chosen over the 'rpeaks'
.. NN and R-peak series provided in [s] format will be converted to [ms] format
.. The ``XX`` in the ``nnXX`` and the ``pnnXX`` keys are substituted by the specified threshold (``threshold``).
For instance, ``nnXX(nni, threshold=30)`` returns the custom ``nn30`` and ``pnn30`` parameters. Using a
``threshold=30`` as ``nnXX(nni, threshold=35`` returns the custom ``nn35`` and ``pnn35`` parameters.
"""
# Check input
nn = tools.check_input(nni, rpeaks)
# Check threshold
if threshold is None:
raise TypeError(
"No threshold specified. Please specify a [ms] threshold.")
if threshold <= 0:
raise ValueError(
"Invalid value for 'threshold'. Value must not be <= 0.")
# Count NN20
nnd = tools.nni_diff(nn)
nnxx = sum(i > threshold for i in nnd)
pnnxx = nnxx / len(nnd) * 100
# Output
args = (nnxx, pnnxx)
names = ('nn%i' % threshold, 'pnn%i' % threshold)
return utils.ReturnTuple(args, names)
def nni_differences_parameters(nni=None, rpeaks=None):
"""Computes basic statistical parameters from a series of successive NN interval differences (mean, min, max, standard deviation).
Parameters
----------
nni : array
NN intervals in [ms] or [s].
rpeaks : array
R-peak times in [ms] or [s].
Returns (biosppy.utils.ReturnTuple Object)
------------------------------------------
[key : format]
Description.
nni_diff_mean: float
Mean NN interval difference [ms].
nni_diff_min : float
Minimum NN interval difference [ms].
nni_diff_max : float
Maximum NN interval difference [ms].
Notes
-----
.. Only one type of input data is required.
.. If both 'nni' and 'rpeaks' are provided, 'nni' will be chosen over the 'rpeaks'
.. NN and R-peak series provided in [s] format will be converted to [ms] format.
"""
# Check input
nn = tools.check_input(nni, rpeaks)
# Get NN interval differences
nnd = tools.nni_diff(nn)
# output
args = (
float(nnd.mean()),
int(nnd.min()),
int(nnd.max()),
)
names = (
'nni_diff_mean',
'nni_diff_min',
'nni_diff_max',
)
return utils.ReturnTuple(args, names)
def sdsd(nni=None, rpeaks=None):
"""Computation of the standard deviation of differences of successive NN intervals.
References: [Electrophysiology1996]
Docs: https://pyhrv.readthedocs.io/en/latest/_pages/api/time.html#sdsd-sdsd
Parameters
----------
nni : array
NN intervals in [ms] or [s].
rpeaks : array
R-peak times in [ms] or [s].
Returns (biosppy.utils.ReturnTuple Object)
------------------------------------------
[key : format]
Description.
sdsd : float
Standard deviation of successive differences of NN intervals [ms]
Notes
-----
.. Only one type of input data is required
.. If both 'nni' and 'rpeaks' are provided, 'nni' will be chosen over the 'rpeaks'
.. NN and R-peak series provided in [s] format will be converted to [ms] format
"""
# Check input
nn = tools.check_input(nni, rpeaks)
# Compute NN differences
nnd = tools.nni_diff(nn)
# Computation of SDNN
sdsd_ = tools.std(nnd)
# Output
args = [sdsd_]
names = ['sdsd']
return utils.ReturnTuple(args, names)
def rmssd(nni=None, rpeaks=None):
"""Computes root mean of squared differences of successive NN Intervals.
References: [Electrophysiology1996], [Lohninger2017]
Docs: https://pyhrv.readthedocs.io/en/latest/_pages/api/time.html#rmssd-rmssd
Parameters
----------
nni : array
NN intervals in [ms] or [s].
rpeaks : array
R-peak times in [ms] or [s].
Returns (biosppy.utils.ReturnTuple Object)
------------------------------------------
[key : format]
Description.
rmssd : float
RMSSD value in [ms].
Notes
-----
.. Only one type of input data is required
.. If both 'nni' and 'rpeaks' are provided, 'nni' will be chosen over the 'rpeaks'
.. NN and R-peak series provided in [s] format will be converted to [ms] format
"""
# Check input
nn = tools.check_input(nni, rpeaks)
# Compute RMSSD
nnd = tools.nni_diff(nn)
rmssd_ = np.sum(x**2 for x in nnd)
rmssd_ = np.sqrt(1. / nnd.size * rmssd_)
# Output
args = (rmssd_, )
names = ('rmssd', )
return utils.ReturnTuple(args, names)
def _computeSignal(self, signal):
obj = {}
# Best min_dist & thres for sphygmogram signal
peaks = peak.indexes(signal, min_dist=56, thres=0.16)
# Ignore un normal signls (with no peaks)
if (len(peaks) == 0): return obj
nn = tools.nn_intervals(peaks)
# Ignore un normal signls (with no NN)
if (len(nn) == 0): return
# Standard
obj = dict(td.nni_parameters(nn, peaks), **obj)
obj = dict(td.nni_differences_parameters(nn, peaks), **obj)
obj = dict(td.sdnn(nn, peaks), **obj)
obj = dict(td.sdnn_index(nn, peaks), **obj)
obj = dict(td.sdann(nn, peaks), **obj)
obj = dict(td.rmssd(nn, peaks), **obj)
obj = dict(td.sdsd(nn, peaks), **obj)
obj = dict(td.nn50(nn, peaks), **obj)
obj = dict(td.nn20(nn, peaks), **obj)
obj = dict(td.geometrical_parameters(nn, peaks, plot=False), **obj)
del obj['nni_histogram']
# Additional
obj = dict({'cv': self._cv(obj['sdnn'], obj['nni_mean'])}, **obj)
peaks_diff = tools.nni_diff(peaks)
obj = dict({'MxDMn': max(peaks_diff) - min(peaks_diff)}, **obj)
obj = dict({'MxRMn': max(peaks_diff) / min(peaks_diff)}, **obj)
obj = dict({'Mo': stats.mode(peaks_diff)[0][0]}, **obj)
counter = Counter(peaks_diff)
idx = list(counter.keys()).index(obj["Mo"])
obj = dict({'AMo': list(counter.values())[idx]}, **obj)
obj = dict({'SI': obj['AMo'] / (2 * obj['Mo'] * obj['MxDMn'])}, **obj)
# Autocorrelation function
# Frequency stats
welch = frequency_domain(signal).stats['welch']['params']
bands = list(welch['fft_bands'].keys())
obj = dict({'TP': welch['fft_total']}, **obj)
obj = dict({'HF': welch['fft_rel'][bands.index('hf')]}, **obj)
obj = dict({'LF': welch['fft_rel'][bands.index('lf')]}, **obj)
obj = dict({'VLF': welch['fft_rel'][bands.index('vlf')]}, **obj)
obj = dict({'ULF': welch['fft_rel'][bands.index('ulf')]}, **obj)
obj = dict({'HFav': welch['fft_abs'][bands.index('hf')]}, **obj)
obj = dict({'LFav': welch['fft_abs'][bands.index('lf')]}, **obj)
obj = dict({'VLFav': welch['fft_abs'][bands.index('vlf')]}, **obj)
obj = dict({'ULFav': welch['fft_abs'][bands.index('ulf')]}, **obj)
obj = dict({'(LF/HF)av': obj['LFav'] / obj['HFav']}, **obj)
obj = dict({'IC': obj['LF'] / obj['VLF']}, **obj)
for k in obj:
if (math.isnan(obj[k])):
obj[k] = 0
return obj