def get_features_dict_ex(signals, filename):
"""
ts : array
Signal time axis reference (seconds).
filtered : array
Filtered ECG signal.
rpeaks : array
R-peak location indices.
templates_ts : array
Templates time axis reference (seconds).
templates : array
Extracted heartbeat templates.
heart_rate_ts : array
Heart rate time axis reference (seconds).
heart_rate : array
Instantaneous heart rate (bpm).
:param x:
:return:
"""
out, rpeaks = ecg.ecgex(signals=signals,
sampling_rate=loader.FREQUENCY,
show=False,
filename=filename)
result = []
for i in range(len(out)):
x = signals.iloc[:, [i]].values.reshape(-1)
[ts, fts, new_rpeaks, tts, thb, hrts, hr] = out[i]
import neurokit as nk
rsa = nk.respiratory_sinus_arrhythmia(rpeaks, rsp_cycles, rsp_signal)
fx = dict()
fx.update(add_suffix(frequency_powers(fts), "fil"))
fx.update(frequency_powers_summary(fts))
fx.update(heart_beats_features(thb, np.median(np.diff(new_rpeaks))))
fx.update(fft_features(heartbeats.median_heartbeat(thb)))
# fx.update(heart_beats_features3(thb))
# gloal feature
fx.update(frequency_powers(x, n_power_features=60))
fx.update(heart_rate_features(hr))
fx.update(r_features(fts, new_rpeaks))
fx['PRbyST'] = fx['PR_interval'] * fx['ST_interval']
fx['P_form'] = fx['P_kurt'] * fx['P_skew']
fx['T_form'] = fx['T_kurt'] * fx['T_skew']
for key, value in fx.items():
if np.math.isnan(value):
value = 0
fx[key] = value
from time_domain_feature import psfeatureTime
result += psfeatureTime(x)
result += list(
np.array([fx[key] for key in sorted(list(fx.keys()))],
dtype=np.float32))
rx = r_features(rpeaks)
result += list(
np.array([rx[key] for key in sorted(list(rx.keys()))],
dtype=np.float32))
return result
def get_features_dict_ex(signals, filename):
"""
ts : array
Signal time axis reference (seconds).
filtered : array
Filtered ECG signal.
rpeaks : array
R-peak location indices.
templates_ts : array
Templates time axis reference (seconds).
templates : array
Extracted heartbeat templates.
heart_rate_ts : array
Heart rate time axis reference (seconds).
heart_rate : array
Instantaneous heart rate (bpm).
:param x:
:return:
"""
out, rpeaks = ecg.ecgex(signals=signals,
sampling_rate=loader.FREQUENCY,
show=False,
filename=filename)
result = []
templates = []
for i in range(len(out)):
x = signals.iloc[:, [i]].values.reshape(-1)
[ts, fts, new_rpeaks, tts, thb, hrts, hr] = out[i]
fx = dict()
fx.update(heart_rate_features(hr))
fx.update(frequency_powers(x, n_power_features=60))
fx.update(add_suffix(frequency_powers(fts), "fil"))
fx.update(frequency_powers_summary(fts))
fx.update(heart_beats_features2(thb, rpeaks))
#fx.update(fft_features(heartbeats.median_heartbeat(thb)))
templates.append(heartbeats.median_heartbeat(thb))
fx['PRbyST'] = fx['PR_interval'] * fx['ST_interval']
fx['P_form'] = fx['P_kurt'] * fx['P_skew']
fx['T_form'] = fx['T_kurt'] * fx['T_skew']
for key, value in fx.items():
if np.math.isnan(value):
value = 0
fx[key] = value
from time_domain_feature import psfeatureTime
result += psfeatureTime(x)
result += list(
np.array([fx[key] for key in sorted(list(fx.keys()))],
dtype=np.float32))
rx = r_features(rpeaks)
result += list(
np.array([rx[key] for key in sorted(list(rx.keys()))],
dtype=np.float32))
import os
#path = os.path.join('data', filename)
#pd.DataFrame(np.array(templates).T).to_csv(path,index=None,header=None)
return result
def get_more_featrues(index_list):
df_info = get_train_info()
train_dir = config.train_dir
level = 9
count = 50
resamples = 2048
columns = [str(i) for i in range(1*(count+8)+1)]
x = pd.DataFrame(columns=columns)
x_index = 0
for i in tqdm(range(len(index_list))):
index = index_list[i]
# print(i, index)
file_name = os.path.join(train_dir, df_info.loc[index,'file'])
from test1 import load_data
raw_df = load_data(file_name)
#print(df.columns.values[:1])
df_list = create_data(raw_df,win_size=2500,move=500)
for df in df_list:
row_features = []
for column in df.columns.values[:1]:
data = df.loc[:, column].values
from scipy import signal
# data = signal.resample(data, resamples)
# data = fiter_wave(data)
# 每列都计算
from time_domain_feature import psfeatureTime
time_features = psfeatureTime(data)
#wavelet_features = calc_energy(data, 'db10', level, "freq", count)
#row_features = row_features + time_features + wavelet_features0000
fxx, pxx = signal.welch(data, 500)
row_features = row_features + time_features+ list(pxx[:count])
x.loc[x_index] = [index]+row_features
x_index += 1
return x
def get_sub_featrues(df_info,train_dir):
level = 9
count = 247
columns = [str(i) for i in range(10*count+1)]
x = pd.DataFrame(columns=columns)
index_list = df_info.index.values
for i in tqdm(range(len(index_list))):
index = index_list[i]
# print(i, index)
file_name = os.path.join(train_dir, df_info.loc[index,'file'])
#print(file_name)
df = load_data(file_name)
row_features = []
#print(df.columns.values[:1])
for column in df.columns.values:
#print(column)
data = df.loc[:, column].values
from scipy import signal
#data = signal.resample(data, resamples)
# 每列都计算
time_features = psfeatureTime(data)
#wavelet_features = calc_energy(data, 'db10', level, "freq", count)
row_features = row_features + time_features
import features.feature_extractor6 as extractor
from loading import loader
from preprocessing import categorizer, balancer, normalizer
from utils import logger, parallel
#data = signal.resample(data, resamples)
# wave filtering
data = normalizer.normalize_ecg(data)
new_feature = extractor.features_for_row(data)
row_features += list(new_feature)
x.loc[i] = [index]+row_features
return x