def extractTimeDomain(self, x):
try:
nni = self.extractRR(x)
nniParams = td.nni_parameters(nni=nni)
nniSD = td.sdnn(nni=nni)
nniDiff = td.nni_differences_parameters(nni=nni)
nniDiffRM = td.rmssd(nni=nni)
nniDiffSD = td.sdsd(nni=nni)
hrParams = td.hr_parameters(nni=nni)
return np.array([nniParams["nni_mean"], nniParams["nni_counter"], nniSD["sdnn"],
nniDiff["nni_diff_mean"], nniDiffRM["rmssd"], nniDiffSD["sdsd"],
hrParams["hr_mean"], hrParams["hr_std"]])
except:
return np.array([])
def compute_features(nni):
features = {}
features['mean_hr'] = tools.heart_rate(nni).mean()
features['sdnn'] = td.sdnn(nni)[0]
features['rmssd'] = td.rmssd(nni)[0]
features['sdsd'] = td.sdsd(nni)[0]
features['nn20'] = td.nn20(nni)[0]
features['pnn20'] = td.nn20(nni)[1]
features['nn50'] = td.nn50(nni)[0]
features['pnn50'] = td.nn50(nni)[1]
features['hf_lf_ratio'] = fd.welch_psd(nni, show=False)['fft_ratio']
features['very_lf'] = fd.welch_psd(nni, show=False)['fft_peak'][0]
features['lf'] = fd.welch_psd(nni, show=False)['fft_peak'][1]
features['hf'] = fd.welch_psd(nni, show=False)['fft_peak'][2]
features['log_very_lf'] = fd.welch_psd(nni, show=False)['fft_log'][0]
features['log_lf'] = fd.welch_psd(nni, show=False)['fft_log'][1]
features['log_hf'] = fd.welch_psd(nni, show=False)['fft_log'][2]
features['sampen'] = nl.sample_entropy(nni)[0]
return features
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 extractTimeDomain(self, x):
try:
nni = self.extractRR(x)
nniParams = td.nni_parameters(nni=nni)
nniSD = td.sdnn(nni=nni)
nniDiff = td.nni_differences_parameters(nni=nni)
nniDiffRM = td.rmssd(nni=nni)
nniDiffSD = td.sdsd(nni=nni)
hrParams = td.hr_parameters(nni=nni)
nn20 = td.nn20(nni=nni)
nn30 = td.nnXX(nni=nni, threshold=30)
nn50 = td.nn50(nni=nni)
# return np.array([nniParams["nni_mean"], nniParams["nni_counter"], nniSD["sdnn"],
# nniDiff["nni_diff_mean"], nniDiffRM["rmssd"], nniDiffSD["sdsd"],
# hrParams["hr_mean"], hrParams["hr_std"]])
return np.array([nniParams["nni_mean"], nniParams["nni_counter"], nniSD["sdnn"],
nniDiff["nni_diff_mean"], nniDiffRM["rmssd"], nniDiffSD["sdsd"],
hrParams["hr_mean"], hrParams["hr_std"], hrParams["hr_max"] - hrParams["hr_min"],
nn20["nn20"], nn20["pnn20"], nn30["nn30"], nn30["pnn30"], nn50["nn50"], nn50["pnn50"]])
except:
return np.array([])
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