-
Notifications
You must be signed in to change notification settings - Fork 28
/
pre_proc.py
233 lines (197 loc) · 9.12 KB
/
pre_proc.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
import wfdb
import matplotlib.pyplot as plt
import numpy as np
from hrv.filters import quotient, moving_median
from scipy import interpolate
from tqdm import tqdm
import pickle
import os
FS = 100.0
# From https://github.com/rhenanbartels/hrv/blob/develop/hrv/classical.py
def create_time_info(rri):
rri_time = np.cumsum(rri) / 1000.0 # make it seconds
return rri_time - rri_time[0] # force it to start at zero
def create_interp_time(rri, fs):
time_rri = create_time_info(rri)
return np.arange(0, time_rri[-1], 1 / float(fs))
def interp_cubic_spline(rri, fs):
time_rri = create_time_info(rri)
time_rri_interp = create_interp_time(rri, fs)
tck = interpolate.splrep(time_rri, rri, s=0)
rri_interp = interpolate.splev(time_rri_interp, tck, der=0)
return time_rri_interp, rri_interp
def interp_cubic_spline_qrs(qrs_index, qrs_amp, fs):
time_qrs = qrs_index / float(FS)
time_qrs = time_qrs - time_qrs[0]
time_qrs_interp = np.arange(0, time_qrs[-1], 1/float(fs))
tck = interpolate.splrep(time_qrs, qrs_amp, s=0)
qrs_interp = interpolate.splev(time_qrs_interp, tck, der=0)
return time_qrs_interp, qrs_interp
data_path = './data/'
train_data_name = ['a02', 'a03', 'a04', 'a05',
'a06', 'a07', 'a08', 'a09', 'a10',
'a11', 'a12', 'a13', 'a14', 'a15',
'a16', 'a17', 'a18', 'a19',
'b02', 'b03', 'b04',
'c02', 'c03', 'c04', 'c05',
'c06', 'c07', 'c08', 'c09',
]
val_data_name = ['a01', 'b01', 'c01']
test_data_name = ['a20','b05','c10']
age = [51, 38, 54, 52, 58,
63, 44, 51, 52, 58,
58, 52, 51, 51, 60,
44, 40, 52, 55, 58,
44, 53, 53, 42, 52,
31, 37, 39, 41, 28,
28, 30, 42, 37, 27]
sex = [1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
0, 1, 1, 1, 1,
1, 1, 1, 0, 0,
0, 0, 1, 1, 1]
def get_qrs_amp(ecg, qrs):
interval = int(FS * 0.250)
qrs_amp = []
for index in range(len(qrs)):
curr_qrs = qrs[index]
amp = np.max(ecg[curr_qrs-interval:curr_qrs+interval])
qrs_amp.append(amp)
return qrs_amp
MARGIN = 10
FS_INTP = 4
MAX_HR = 300.0
MIN_HR = 20.0
MIN_RRI = 1.0 / (MAX_HR / 60.0) * 1000
MAX_RRI = 1.0 / (MIN_HR / 60.0) * 1000
train_input_array = []
train_label_array = []
for data_index in range(len(train_data_name)):
print (train_data_name[data_index])
win_num = len(wfdb.rdann(os.path.join(data_path,train_data_name[data_index]), 'apn').symbol)
signals, fields = wfdb.rdsamp(os.path.join(data_path,train_data_name[data_index]))
for index in tqdm(range(1, win_num)):
samp_from = index * 60 * FS # 60 seconds
samp_to = samp_from + 60 * FS # 60 seconds
qrs_ann = wfdb.rdann(data_path + train_data_name[data_index], 'qrs', sampfrom=samp_from - (MARGIN*100), sampto=samp_to + (MARGIN*100)).sample
apn_ann = wfdb.rdann(data_path + train_data_name[data_index], 'apn', sampfrom=samp_from, sampto=samp_to-1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
try:
rri_filt = moving_median(rri_ms)
if len(rri_filt) > 5 and (np.min(rri_filt) >= MIN_RRI and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN) & (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
skip = 0
if skip == 0:
rri_intp = rri_intp - np.mean(rri_intp)
qrs_intp = qrs_intp - np.mean(qrs_intp)
if apn_ann[0] == 'N': # Normal
label = 0.0
elif apn_ann[0] == 'A': # Apnea
label = 1.0
else:
label = 2.0
train_input_array.append([rri_intp, qrs_intp, age[data_index], sex[data_index]])
train_label_array.append(label)
except:
hrv_module_error = 1
with open('train_input.pickle','wb') as f:
pickle.dump(train_input_array, f)
with open('train_label.pickle','wb') as f:
pickle.dump(train_label_array, f)
val_input_array = []
val_label_array = []
for data_index in range(len(val_data_name)):
print (val_data_name[data_index])
win_num = len(wfdb.rdann(os.path.join(data_path,val_data_name[data_index]), 'apn').symbol)
signals, fields = wfdb.rdsamp(os.path.join(data_path,val_data_name[data_index]))
for index in tqdm(range(1, win_num)):
samp_from = index * 60 * FS # 60 seconds
samp_to = samp_from + 60 * FS # 60 seconds
qrs_ann = wfdb.rdann(data_path + val_data_name[data_index], 'qrs', sampfrom=samp_from - (MARGIN*100), sampto=samp_to + (MARGIN*100)).sample
apn_ann = wfdb.rdann(data_path + val_data_name[data_index], 'apn', sampfrom=samp_from, sampto=samp_to-1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
try:
rri_filt = moving_median(rri_ms)
if len(rri_filt) > 5 and (np.min(rri_filt) >= MIN_RRI and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN) & (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
skip = 0
if skip == 0:
rri_intp = rri_intp - np.mean(rri_intp)
qrs_intp = qrs_intp - np.mean(qrs_intp)
if apn_ann[0] == 'N': # Normal
label = 0.0
elif apn_ann[0] == 'A': # Apnea
label = 1.0
else:
label = 2.0
val_input_array.append([rri_intp, qrs_intp, age[data_index], sex[data_index]])
val_label_array.append(label)
except:
hrv_module_error = 1
with open('val_input.pickle','wb') as f:
pickle.dump(val_input_array, f)
with open('val_label.pickle','wb') as f:
pickle.dump(val_label_array, f)
test_input_array = []
test_label_array = []
for data_index in range(len(test_data_name)):
print (test_data_name[data_index])
win_num = len(wfdb.rdann(os.path.join(data_path,test_data_name[data_index]), 'apn').symbol)
signals, fields = wfdb.rdsamp(os.path.join(data_path,test_data_name[data_index]))
for index in tqdm(range(1, win_num)):
samp_from = index * 60 * FS # 60 seconds
samp_to = samp_from + 60 * FS # 60 seconds
qrs_ann = wfdb.rdann(data_path + test_data_name[data_index], 'qrs', sampfrom=samp_from - (MARGIN*100), sampto=samp_to + (MARGIN*100)).sample
apn_ann = wfdb.rdann(data_path + test_data_name[data_index], 'apn', sampfrom=samp_from, sampto=samp_to-1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
try:
rri_filt = moving_median(rri_ms)
if len(rri_filt) > 5 and (np.min(rri_filt) >= MIN_RRI and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN) & (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
skip = 0
if skip == 0:
rri_intp = rri_intp - np.mean(rri_intp)
qrs_intp = qrs_intp - np.mean(qrs_intp)
if apn_ann[0] == 'N': # Normal
label = 0.0
elif apn_ann[0] == 'A': # Apnea
label = 1.0
else:
label = 2.0
test_input_array.append([rri_intp, qrs_intp, age[data_index], sex[data_index]])
test_label_array.append(label)
except:
hrv_module_error = 1
with open('test_input.pickle','wb') as f:
pickle.dump(test_input_array, f)
with open('test_label.pickle','wb') as f:
pickle.dump(test_label_array, f)