def _poincare(self, nn, peaks):
res = poincare(nni=nn, rpeaks=peaks, show=False)
nn_mean = np.mean(nn)
sd1, sd2 = res['sd1'], res['sd2']
ellipse = mpl.patches.Ellipse((nn_mean, nn_mean),
sd1 * 2,
sd2 * 2,
angle=-45,
fc='k',
zorder=1)
ellipse = ellipse.get_verts()
ellipse = {'x': ellipse[:, 0], 'y': ellipse[:, 1]}
obj = {'sd1': res['sd1'], 'sd2': res['sd2'], 'ellipse': ellipse}
return obj
def extractNonLinearDomain(self, x):
try:
nni = self.extractRR(x)
sampEntro = nn.sample_entropy(nni=nni, dim=2) #change dim from 1 to 2
lyapEx = self.lyapunov_exponent(nni=nni, emb_dim=3, matrix_dim=2)
pointCare = nn.poincare(nni=nni, show=False)
# hrust = nolds.hurst_rs(nni)
corrDim = nolds.corr_dim(nni, emb_dim=3)
# csi = get_csi_cvi_features(nni) #caused worse results
# geo = get_geometrical_features(nni) #caused worse results
# dfa = nolds.dfa(nni)
# return np.array([sampEntro["sampen"], lyapEx["lyapex"]])
return np.array([sampEntro["sampen"], lyapEx["lyapex"], pointCare["sd1"], pointCare["sd2"], pointCare["sd_ratio"], pointCare["ellipse_area"], corrDim])
except:
return np.array([])
def cal_hrv(data_list):
if (np.nan in data_list) or (len(data_list) <= 1):
sdsd = np.nan
rmssd = np.nan
sd1 = np.nan
sd2 = np.nan
sd_ratio = np.nan
a1 = np.nan
a2 = np.nan
a_ratio = np.nan
sampen = np.nan
else:
# sdsd
sdsd = round(td.sdsd(nni=data_list)['sdsd'], 5)
# rmssd
rmssd = round(td.rmssd(nni=data_list)['rmssd'], 5)
# sd1, sd2
nl_results = nl.poincare(nni=data_list)
sd1 = round(nl_results['sd1'], 5)
sd2 = round(nl_results['sd2'], 5)
sd_ratio = round(sd2 / sd1, 5)
# dfa a1, a2
dfa_results = nl.dfa(data_list)
try:
a1 = round(dfa_results['dfa_alpha1'], 5)
a2 = round(dfa_results['dfa_alpha2'], 5)
a_ratio = round(a2 / a1, 5)
except:
a1 = np.nan
print(a1, type(a1))
a2 = np.nan
print(a2, type(a2))
a_ratio = np.nan
# Sampen
t = np.std(data_list)
sampen = round(
nl.sample_entropy(nni=data_list, tolerance=t)['sampen'], 6)
return sdsd, rmssd, sd1, sd2, sd_ratio, a1, a2, a_ratio, sampen
def resp_features(resp_peaks):
bvp = tools.nn_intervals(resp_peaks.tolist())
resp_features = {}
#------時系列解析------#
L = len(resp_peaks)
resp_features['bvp_mean'] = np.mean(bvp)
resp_features['bvp_max'] = np.max(bvp)
resp_features['bvp_min'] = np.min(bvp)
resp_features['bvp_sdnn'] = np.std(bvp)
resp_features['bvp_sdsd'] = np.std(np.diff(bvp))
resp_features['bvp_rmssd'] = np.sqrt((1 / L) * sum(np.diff(bvp)**2))
resp_features['bvp_median'] = np.median(bvp)
#-----ポアンカレプロット-----#
_, resp_features['bvp_sd1'], resp_features['bvp_sd2'], resp_features[
'bvp_sd_ratio'], resp_features['bvp_ellipse_area'] = nl.poincare(
rpeaks=resp_peaks.astype(int).tolist(), show=False)
#------MultiScaleEntropy-----#
# 後で追加すること
return resp_features
def Poincare_Windowing(rr_signal, plotter=False):
"""
rr_signal :: RR vector of time in seconds
plotter :: Boolean to plot the Poincare output or not.
"""
poin_r = list()
for idx in range(0, len(rr_signal) - RR_WLEN, RR_STEP):
window_slice = slice(idx, idx + RR_WLEN)
rr_window = rr_signal[window_slice]
if plotter == True:
poincare_results = nl.poincare(rr_window,
show=True,
figsize=None,
ellipse=True,
vectors=True,
legend=True)
poin_r.append(poincare_results["sd_ratio"])
elif plotter == False:
poincare_results = poincarePlot(rr_window, show=False)
poin_r.append(poincare_results["sd_ratio"])
return poin_r
def _prepare_figures(self):
"""Re-creates plot figures based on the results for the PDF report"""
plots = {}
# Tachogram
try:
fig = tools.tachogram(nni=self.nni,
show=False,
figsize=self.figsizes,
interval='complete')['tachogram_plot']
plots['tachogram'] = fig
self._set_section('tachogram')
except Exception as e:
self._set_section('tachogram', False)
warnings.warn(
"\nAn error occurred while trying to create the Tachogram figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# ECG signal plot
try:
if self.signal is not None:
plots['ecg_plot'] = \
tools.plot_ecg(signal=self.signal, show=False, interval='complete', figsize=self.figsizes)['ecg_plot']
self._set_section('ecg_plot')
except Exception as e:
self._set_section('ecg_plot', False)
warnings.warn(
"\nAn error occurred while trying to create the ECG plot figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# Histogram
try:
plots['histogram'] = td.triangular_index(
nni=self.nni, show=False, legend=False,
figsize=self.figsizes)['tri_histogram']
self._set_section('histogram')
except Exception as e:
self._set_section('histogram', False)
warnings.warn(
"\nAn error occurred while trying to create the NNI histogram figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# HR Heat Plot
if isinstance(self._general_info['age'], int) and isinstance(
self._general_info['gender'], str):
try:
plots['hr_heatplot'] = \
pyhrv.utils.heart_rate_heatplot(nni=self.nni, show=False, age=self._general_info['age'],
gender=self._general_info['gender'], figsize=self.figsizes)['hr_heatplot']
self._set_section('hrheatplot')
except Exception as e:
self._set_section('hrheatplot', False)
warnings.warn(
"\nAn error occurred while trying to create the HR heatplotfor "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# Welch's Plot
if 'fft_plot' in self.results.keys():
try:
plots['fft_plot'] = \
fd.welch_psd(nni=self.nni, fbands=self.results['fft_bands'], window=self.results['fft_window'],
show=False, show_param=False, figsize=self.figsizes)['fft_plot']
self._set_section('fft_plot')
except Exception as e:
self._set_section('fft_plot', False)
warnings.warn(
"\nAn error occurred while trying to create the FFT/Welch figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# Welch's Plot
if 'ar_plot' in self.results.keys():
try:
plots['ar_plot'] = \
fd.ar_psd(nni=self.nni, fbands=self.results['ar_bands'], show=False, show_param=False,
figsize=self.figsizes)['ar_plot']
self._set_section('ar_plot')
except Exception as e:
self._set_section('ar_plot', False)
warnings.warn(
"\nAn error occurred while trying to create the AR PSD figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# Welch's Plot
if 'lomb_plot' in self.results.keys():
try:
plots['lomb_plot'] = \
fd.lomb_psd(nni=self.nni, fbands=self.results['lomb_bands'], ma_size=self.results['lomb_ma'],
show=False, show_param=False, figsize=self.figsizes)['lomb_plot']
self._set_section('lomb_plot')
except Exception as e:
self._set_section('lomb_plot', False)
warnings.warn(
"\nAn error occurred while trying to create the AR PSD figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# Poincare
if 'poincare_plot' in self.results.keys():
try:
plots['poincare_plot'] = nl.poincare(
nni=self.nni, show=False, legend=False)['poincare_plot']
self._set_section('poincare_plot')
except Exception as e:
self._set_section('poincare_plot', False)
warnings.warn(
"\nAn error occurred while trying to create the Poincare plot figure for "
"the PDF report: \n%s'" % str(e),
stacklevel=2)
# DFA
if 'dfa_plot' in self.results.keys():
try:
fig = nl.dfa(nn=self.nni, show=False, legend=False)['dfa_plot']
plots['dfa_plot'] = fig
self._set_section('dfa')
except Exception as e:
self._set_section('dfa', False)
warnings.warn(
"\nAn error occurred while trying to create the DFA plot figure for "
"the PDF report: %s'" % str(e),
stacklevel=2)
# Save all plot figures
for f in plots.keys():
plots[f].savefig("%s%s.png" %
(self._figure_path, f.replace('_', '')),
dpi=300,
bbox_inches='tight')
def poincare_plot(nni, title=''):
nl.poincare(nni, title=title)