def getMultiScaleEntropyWithWindow(signal,
type="freq",
window_size=40,
step_size=20):
entropy = []
step_size = window_size
i = 0
while (i < signal.shape[1]):
j = 0
she_feat = []
while (j < signal.shape[0]):
print i
if (j + window_size < signal.shape[0]):
sh_ent = ent.multiscale_entropy(
signal[j:j + window_size + 1, i],
len(signal[j:j + window_size + 1, i]),
0.2 * np.std(signal[j:j + window_size + 1, i]))
#print sh_ent
she_feat.append(sh_ent)
else:
sh_ent = ent.multiscale_entropy(
signal[j:j + window_size + 1, i],
len(signal[j:j + window_size + 1, i]),
0.2 * np.std(signal[j:j + window_size + 1, i]))
#print sh_ent
she_feat.append(sh_ent)
j += step_size
entropy.append(she_feat)
i += 1
return entropy
def compute_multiscale_entropy(flux):
"""
Returns the multiscale entropy
Inputs
-----------------
flux:
flux data
Returns
-----------------
mean: float
mean of the mse curve
max_v: float
maximum of the mse curve
stdev: float
standard deviation of the mse curve
power: float
power present in the mse curve
"""
ms_ent = ent.multiscale_entropy(flux, 2, maxscale=10)
mean = np.mean(ms_ent)
stdev = np.std(ms_ent)
max_v = np.max(ms_ent)
power = 1 / len(ms_ent) * np.sum(ms_ent**2)
return mean, max_v, stdev, power
def test_multiScaleEntropy(self):
multi_scale_entropy = ent.multiscale_entropy(RANDOM_TIME_SERIES,
4,
maxscale=4)
np.testing.assert_allclose(
multi_scale_entropy,
np.array([2.52572864, 2.31911439, 1.65292302, 1.86075234]))
def test_multiScaleEntropy(self):
multi_scale_entropy = ent.multiscale_entropy(RANDOM_TIME_SERIES,
4,
maxscale=4)
np.testing.assert_array_equal(
np.round(multi_scale_entropy, 8),
np.array([2.52572864, 2.33537492, 1.65292302, 1.86075234]))
def test_multiScaleEntropy(self):
ts = TS_SAMPLE_ENTROPY
std_ts = np.std(ts)
multi_scale_entropy = ent.multiscale_entropy(ts, 4, 0.2 * std_ts)
np.testing.assert_array_equal(
np.round(multi_scale_entropy, 4),
np.array([2.2119, 2.6221, 1.7473, 1.9902]))
def computeShannonEntropy(signal):
she_feat = [0] * signal.shape[1]
# compute fft for each channel
i = 0
while (i < signal.shape[1]):
print i
she_feat[i] = ent.multiscale_entropy(signal[:, i], 2,
0.1 * np.std(signal[:, i]))
i += 1
return she_feat
def multiscale_entropy(x, sample_length=2, maxscale=20):
"""Return multiscale entropy of the given signal
Args:
:param x: 1-dim list
Return:
the multiscale entropy of x (returned type: list of float)
"""
return ent.multiscale_entropy(x,
sample_length=sample_length,
maxscale=maxscale)
def multi_entrp(l):
"""
Multiscale entropy
:param l:
:return:
"""
start = time.time()
me = e.multiscale_entropy(l)
elapsed_time = time.time() - start
logger.debug("Elapsed time to calculate Multiscale entropy value is %s",
elapsed_time)
return me
def computeMSE(self, index, m_length=20):
indexStart = index['indexStart']
indexEnd = index['indexEnd']
dfData = self.xData.loc[indexStart:indexEnd]
MSE_means = []
for feature in self.xFeatures:
ts = list(dfData[feature])
tsStd = np.std(ts)
MSEs = ent.multiscale_entropy(ts,
sample_length=m_length,
tolerance=0.2 * tsStd)
MSE_means.append(np.mean(MSEs))
return MSE_means
def sig_sample_entropy(sig):
sig_std = np.std(sig)
return ent.multiscale_entropy(sig, 4, 0.2 * sig_std)[0]
def calculate_rri_nonlinear_statistics(self,
rri,
diff_rri,
diff2_rri,
suffix=''):
# Empty dictionary
rri_nonlinear_statistics = dict()
# Non-linear RR statistics
if len(rri) > 1:
rri_nonlinear_statistics['rri_approximate_entropy' + suffix] = \
self.safe_check(pyeeg.ap_entropy(rri, M=2, R=0.1*np.std(rri)))
rri_nonlinear_statistics['rri_sample_entropy' + suffix] = \
self.safe_check(ent.sample_entropy(rri, sample_length=2, tolerance=0.1*np.std(rri))[0])
rri_nonlinear_statistics['rri_multiscale_entropy' + suffix] = \
self.safe_check(ent.multiscale_entropy(rri, sample_length=2, tolerance=0.1*np.std(rri))[0])
rri_nonlinear_statistics['rri_permutation_entropy' + suffix] = \
self.safe_check(ent.permutation_entropy(rri, m=2, delay=1))
rri_nonlinear_statistics['rri_multiscale_permutation_entropy' + suffix] = \
self.safe_check(ent.multiscale_permutation_entropy(rri, m=2, delay=1, scale=1)[0])
rri_nonlinear_statistics['rri_fisher_info' + suffix] = fisher_info(
rri, tau=1, de=2)
hjorth_parameters = hjorth(rri)
rri_nonlinear_statistics['rri_activity' +
suffix] = hjorth_parameters[0]
rri_nonlinear_statistics['rri_complexity' +
suffix] = hjorth_parameters[1]
rri_nonlinear_statistics['rri_morbidity' +
suffix] = hjorth_parameters[2]
rri_nonlinear_statistics['rri_hurst_exponent' + suffix] = pfd(rri)
rri_nonlinear_statistics['rri_svd_entropy' + suffix] = svd_entropy(
rri, tau=2, de=2)
rri_nonlinear_statistics['rri_petrosian_fractal_dimension' +
suffix] = pyeeg.pfd(rri)
else:
rri_nonlinear_statistics['rri_approximate_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['rri_sample_entropy' + suffix] = np.nan
rri_nonlinear_statistics['rri_multiscale_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['rri_permutation_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['rri_multiscale_permutation_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['rri_fisher_info' + suffix] = np.nan
rri_nonlinear_statistics['rri_activity' + suffix] = np.nan
rri_nonlinear_statistics['rri_complexity' + suffix] = np.nan
rri_nonlinear_statistics['rri_morbidity' + suffix] = np.nan
rri_nonlinear_statistics['rri_hurst_exponent' + suffix] = np.nan
rri_nonlinear_statistics['rri_svd_entropy' + suffix] = np.nan
rri_nonlinear_statistics['rri_petrosian_fractal_dimension' +
suffix] = np.nan
# Non-linear RR difference statistics
if len(diff_rri) > 1:
rri_nonlinear_statistics['diff_rri_approximate_entropy' + suffix] = \
self.safe_check(pyeeg.ap_entropy(diff_rri, M=2, R=0.1*np.std(rri)))
rri_nonlinear_statistics['diff_rri_sample_entropy' + suffix] = \
self.safe_check(ent.sample_entropy(diff_rri, sample_length=2, tolerance=0.1*np.std(rri))[0])
rri_nonlinear_statistics['diff_rri_multiscale_entropy' + suffix] = \
self.safe_check(ent.multiscale_entropy(diff_rri, sample_length=2, tolerance=0.1*np.std(rri))[0])
rri_nonlinear_statistics['diff_rri_permutation_entropy' + suffix] = \
self.safe_check(ent.permutation_entropy(diff_rri, m=2, delay=1))
rri_nonlinear_statistics['diff_rri_multiscale_permutation_entropy' + suffix] = \
self.safe_check(ent.multiscale_permutation_entropy(diff_rri, m=2, delay=1, scale=1)[0])
rri_nonlinear_statistics['diff_rri_fisher_info' +
suffix] = fisher_info(diff_rri,
tau=1,
de=2)
hjorth_parameters = hjorth(diff_rri)
rri_nonlinear_statistics['diff_rri_activity' +
suffix] = hjorth_parameters[0]
rri_nonlinear_statistics['diff_rri_complexity' +
suffix] = hjorth_parameters[1]
rri_nonlinear_statistics['diff_rri_morbidity' +
suffix] = hjorth_parameters[2]
rri_nonlinear_statistics['diff_rri_hurst_exponent' +
suffix] = pfd(diff_rri)
rri_nonlinear_statistics['diff_rri_svd_entropy' +
suffix] = svd_entropy(diff_rri,
tau=2,
de=2)
rri_nonlinear_statistics['diff_rri_petrosian_fractal_dimension' +
suffix] = pyeeg.pfd(diff_rri)
else:
rri_nonlinear_statistics['diff_rri_approximate_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff_rri_sample_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff_rri_multiscale_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff_rri_permutation_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff_rri_multiscale_permutation_entropy'
+ suffix] = np.nan
rri_nonlinear_statistics['diff_rri_fisher_info' + suffix] = np.nan
rri_nonlinear_statistics['diff_rri_activity' + suffix] = np.nan
rri_nonlinear_statistics['diff_rri_complexity' + suffix] = np.nan
rri_nonlinear_statistics['diff_rri_morbidity' + suffix] = np.nan
rri_nonlinear_statistics['diff_rri_hurst_exponent' +
suffix] = np.nan
rri_nonlinear_statistics['diff_rri_svd_entropy' + suffix] = np.nan
rri_nonlinear_statistics['diff_rri_petrosian_fractal_dimension' +
suffix] = np.nan
# Non-linear RR difference difference statistics
if len(diff2_rri) > 1:
rri_nonlinear_statistics['diff2_rri_shannon_entropy' + suffix] = \
self.safe_check(ent.shannon_entropy(diff2_rri))
rri_nonlinear_statistics['diff2_rri_approximate_entropy' + suffix] = \
self.safe_check(pyeeg.ap_entropy(diff2_rri, M=2, R=0.1*np.std(rri)))
rri_nonlinear_statistics['diff2_rri_sample_entropy' + suffix] = \
self.safe_check(ent.sample_entropy(diff2_rri, sample_length=2, tolerance=0.1*np.std(rri))[0])
rri_nonlinear_statistics['diff2_rri_multiscale_entropy' + suffix] = \
self.safe_check(ent.multiscale_entropy(diff2_rri, sample_length=2, tolerance=0.1*np.std(rri))[0])
rri_nonlinear_statistics['diff2_rri_permutation_entropy' + suffix] = \
self.safe_check(ent.permutation_entropy(diff2_rri, m=2, delay=1))
rri_nonlinear_statistics['diff2_rri_multiscale_permutation_entropy' + suffix] = \
self.safe_check(ent.multiscale_permutation_entropy(diff2_rri, m=2, delay=1, scale=1)[0])
rri_nonlinear_statistics['diff2_rri_fisher_info' +
suffix] = fisher_info(diff2_rri,
tau=1,
de=2)
hjorth_parameters = hjorth(diff2_rri)
rri_nonlinear_statistics['diff2_rri_activity' +
suffix] = hjorth_parameters[0]
rri_nonlinear_statistics['diff2_rri_complexity' +
suffix] = hjorth_parameters[1]
rri_nonlinear_statistics['diff2_rri_morbidity' +
suffix] = hjorth_parameters[2]
rri_nonlinear_statistics['diff2_rri_hurst_exponent' +
suffix] = pfd(diff2_rri)
rri_nonlinear_statistics['diff2_rri_svd_entropy' +
suffix] = svd_entropy(diff2_rri,
tau=2,
de=2)
rri_nonlinear_statistics['diff2_rri_petrosian_fractal_dimension' +
suffix] = pyeeg.pfd(diff2_rri)
else:
rri_nonlinear_statistics['diff2_rri_shannon_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_approximate_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_sample_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_multiscale_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_permutation_entropy' +
suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_multiscale_permutation_entropy'
+ suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_fisher_info' + suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_activity' + suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_complexity' + suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_morbidity' + suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_hurst_exponent' +
suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_svd_entropy' + suffix] = np.nan
rri_nonlinear_statistics['diff2_rri_petrosian_fractal_dimension' +
suffix] = np.nan
return rri_nonlinear_statistics
