-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
366 lines (314 loc) · 12 KB
/
utils.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
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
import json
import numpy as np
import sys
import matplotlib.pyplot as plt
import pandas as pd
import peakutils
from sklearn import preprocessing
from scipy import signal
"""
Written by: Xingyao Wang, Chengyu Liu
School of Instrument Science and Engineering
Southeast University, China
"""
def p_t_qrs(ecg_original, fs=1000, gr=1):
delay = 0
skip = 0
m_selected_RR = 0
mean_RR = 0
ser_back = 0
if (fs == 200):
# Low pass and High pass
# Low pass
wn = 12 * 2 / fs
N = 3
a, b = signal.butter(N, wn, 'low')
ecg_l = signal.filtfilt(a, b, ecg_original)
ecg_l = ecg_l / max(abs(ecg_l))
ecg_l = np.around(ecg_l, decimals=4)
# High pass
wn = 5 * 2 / fs
N = 3
a, b = signal.butter(N, wn, 'high')
ecg_h = signal.filtfilt(a, b, ecg_original)
ecg_h = ecg_h / max(abs(ecg_h))
else:
# Bandpass
f1 = 5
f2 = 15
wn = []
wn.append(f1 * 2 / fs)
wn.append(f2 * 2 / fs)
N = 3
a, b = signal.butter(N, wn, 'bandpass')
ecg_h = signal.filtfilt(a, b, ecg_original)
ecg_h = ecg_h / max(abs(ecg_h))
# Derivative
int_c = (5 - 1) / (fs * 1 / 40)
x = np.arange(1,6)
xp = np.dot(np.array([1, 2, 0, -2, -1]), (1 / 8) * fs)
fp = np.arange(1,5+int_c,int_c)
b = np.interp(fp, x, xp)
ecg_d = signal.filtfilt(b, 1, ecg_h)
ecg_d = ecg_d / max(ecg_d)
# Squaring and Moving average
ecg_s = np.power(ecg_d, 2)
ecg_m = np.convolve(ecg_s ,np.ones(int(np.around(0.150*fs)))/np.around(0.150*fs))
delay = delay + np.around(0.150*fs) / 2
# Fiducial Marks
locs = peakutils.indexes(ecg_m, thres=0, min_dist=np.around(0.2 * fs))
pks = ecg_m[locs[:]]
# Init other parameters
LLp = len(pks)
qrs_c = np.zeros(LLp)
qrs_i = np.zeros(LLp)
qrs_i_raw = np.zeros(LLp)
qrs_amp_raw= np.zeros(LLp)
nois_c = np.zeros(LLp)
nois_i = np.zeros(LLp)
SIGL_buf = np.zeros(LLp)
NOISL_buf = np.zeros(LLp)
SIGL_buf1 = np.zeros(LLp)
NOISL_buf1 = np.zeros(LLp)
THRS_buf1 = np.zeros(LLp)
THRS_buf = np.zeros(LLp)
# Init training phase
THR_SIG = max(ecg_m[0:2*fs])*1/3
THR_NOISE = np.mean(ecg_m[0:2*fs])*1/2
SIG_LEV= THR_SIG
NOISE_LEV = THR_NOISE
# Init bandpath filter threshold
THR_SIG1 = max(ecg_h[0:2*fs])*1/3
THR_NOISE1 = np.mean(ecg_h[0:2*fs])*1/2
SIG_LEV1 = THR_SIG1
NOISE_LEV1 = THR_NOISE1
# Thresholding and desicion rule
Beat_C = -1
Beat_C1 = -1
Noise_Count = 0
for i in range(LLp):
if ((locs[i] - np.around(0.150*fs)) >= 1 and (locs[i] <= len(ecg_h))):
_start = locs[i] - np.around(0.15*fs).astype(int)
_ = ecg_h[_start:locs[i]]
y_i = max(_)
x_i = np.argmax(_)
else:
if i == 0:
y_i = max(ecg_h[0:locs[i]])
x_i = np.argmax(ecg_h[0:locs[i]])
ser_back = 1
elif (locs[i] >= len(ecg_h)):
_ = ecg_h[locs[i] - np.around(0.150*fs).astype(int):]
y_i = max(_)
x_i = np.argmax(_)
# Update the heart_rate
if (Beat_C >= 9):
diffRR = np.diff(qrs_i[Beat_C-8:Beat_C])
mean_RR = np.mean(diffRR)
comp = qrs_i[Beat_C] - qrs_i[Beat_C-1]
if ((comp <= 0.92*mean_RR) or (comp >= 1.16*mean_RR)):
THR_SIG = 0.5*(THR_SIG)
THR_SIG1 = 0.5*(THR_SIG1)
else:
m_selected_RR = mean_RR
# Calculate the mean last 8 R waves to ensure that QRS is not
if m_selected_RR:
test_m = m_selected_RR
elif (mean_RR and m_selected_RR == 0):
test_m = mean_RR
else:
test_m = 0
if test_m:
if ((locs[i] - qrs_i[Beat_C]) >= np.around(1.66*test_m)):
_start = int(qrs_i[Beat_C] + np.around(0.20*fs))
_end = int(locs[i] - np.around(0.20*fs))
pks_temp = max(ecg_m[_start:_end+1])
locs_temp = np.argmax(ecg_m[_start:_end+1])
locs_temp = qrs_i[Beat_C] + np.around(0.20*fs) + locs_temp - 1
if (pks_temp > THR_NOISE):
Beat_C += 1
qrs_c[Beat_C] = pks_temp
qrs_i[Beat_C] = locs_temp
if (locs_temp <= len(ecg_h)):
_start = int(locs_temp - np.around(0.150*fs))
_end = int(locs_temp + 1)
y_i_t = max(ecg_h[_start:_end])
x_i_t = np.argmax(ecg_h[_start:_end])
else:
_ = locs_temp - np.around(0.150*fs)
y_i_t = max(ecg_h[_:])
x_i_t = np.argmax(ecg_h[_:])
if (y_i_t > THR_NOISE1):
Beat_C1 += 1
qrs_i_raw[Beat_C1] = locs_temp - np.around(0.150*fs) + (x_i_t - 1)
qrs_amp_raw[Beat_C1] = y_i_t
SIG_LEV1 = 0.25*y_i_t + 0.75*SIG_LEV1
not_nois = 1
SIG_LEV = 0.25*pks_temp + 0.75*SIG_LEV
else:
not_nois = 0
# Find noise and QRS peaks
if (pks[i] >= THR_SIG):
if (Beat_C >= 3):
if ((locs[i] - qrs_i[Beat_C]) <= np.around(0.3600*fs)):
_start = locs[i] - np.around(0.075*fs).astype('int')
Slope1 = np.mean(np.diff(ecg_m[_start:locs[i]]))
_start = int(qrs_i[Beat_C] - np.around(0.075*fs))
_end = int(qrs_i[Beat_C])
Slope2 = np.mean(np.diff(ecg_m[_start:_end]))
if abs(Slope1) <= abs(0.5*(Slope2)):
nois_c[Noise_Count] = pks[i]
nois_i[Noise_Count] = locs[i]
Noise_Count += 1
skip = 1
NOISE_LEV1 = 0.125*y_i + 0.875*NOISE_LEV1
NOISE_LEV = 0.125*pks[i] + 0.875*NOISE_LEV
else:
skip = 0
if (skip == 0):
Beat_C += 1
qrs_c[Beat_C] = pks[i]
qrs_i[Beat_C] = locs[i]
if (y_i >= THR_SIG1):
Beat_C1 += 1
if ser_back:
qrs_i_raw[Beat_C1] = x_i
else:
qrs_i_raw[Beat_C1] = locs[i] - np.around(0.150*fs) + (x_i - 1)
qrs_amp_raw[Beat_C1] = y_i
SIG_LEV1 = 0.125*y_i + 0.875*SIG_LEV1
SIG_LEV = 0.125*pks[i] + 0.875*SIG_LEV
elif ((THR_NOISE <= pks[i]) and (pks[i] < THR_SIG)):
NOISE_LEV1 = 0.125*y_i + 0.875*NOISE_LEV1
NOISE_LEV = 0.125*pks[i] + 0.875*NOISE_LEV
elif (pks[i] < THR_NOISE):
nois_c[Noise_Count] = pks[i]
nois_i[Noise_Count] = locs[i]
NOISE_LEV1 = 0.125*y_i + 0.875*NOISE_LEV1
NOISE_LEV = 0.125*pks[i] + 0.875*NOISE_LEV
Noise_Count += 1
# Adjust the threshold with SNR
if (NOISE_LEV != 0 or SIG_LEV != 0):
THR_SIG = NOISE_LEV + 0.25*(abs(SIG_LEV - NOISE_LEV))
THR_NOISE = 0.5*(THR_SIG)
if (NOISE_LEV1 != 0 or SIG_LEV1 != 0):
THR_SIG1 = NOISE_LEV1 + 0.25*(abs(SIG_LEV1 - NOISE_LEV1))
THR_NOISE1 = 0.5*(THR_SIG1)
SIGL_buf[i] = SIG_LEV
NOISL_buf[i] = NOISE_LEV
THRS_buf[i] = THR_SIG
SIGL_buf1[i] = SIG_LEV1
NOISL_buf1[i] = NOISE_LEV1
THRS_buf1[i] = THR_SIG1
skip = 0
not_nois = 0
ser_back = 0
# Adjust lengths
qrs_i_raw = qrs_i_raw[0:Beat_C1+1]
qrs_amp_raw = qrs_amp_raw[0:Beat_C1+1]
qrs_c = qrs_c[0:Beat_C+1]
qrs_i = qrs_i[0:Beat_C+1]
return qrs_i_raw
def qrs_detect(ECG, fs):
winsize = 5 * fs * 60 # 5min 滑窗
#winsize = 10 * fs # 10s 滑窗
NB_SAMP = len(ECG)
peaks = []
if NB_SAMP < winsize:
peaks.extend(p_t_qrs(ECG, fs))
peaks = np.array(peaks)
peaks = np.delete(peaks, np.where(peaks >= NB_SAMP-2*fs)[0]) # 删除最后2sR波位置
else:
# 5分钟滑窗检测,重叠5s数据
count = NB_SAMP // winsize
for j in range(count+1):
if j == 0:
ecg_data = ECG[j*winsize: (j+1)*winsize]
peak = p_t_qrs(ecg_data, fs)
peak = np.array(peak)
peak = np.delete(peak, np.where(peak >= winsize-2*fs)[0]).tolist() # 删除5分钟窗口最后2sR波位置
peaks.extend(map(lambda n: n+j*winsize, peak))
elif j == count:
ecg_data = ECG[j*winsize-5*fs: ]
if len(ecg_data) == 0:
pass
else:
peak = p_t_qrs(ecg_data, fs)
peak = np.array(peak)
peak = np.delete(peak, np.where(peak <= 2*fs)[0]).tolist() # 删除最后多余窗口前2sR波位置
peaks.extend(map(lambda n: n+j*winsize-5*fs, peak))
else:
ecg_data = ECG[j*winsize-5*fs: (j+1)*winsize]
peak = p_t_qrs(ecg_data, fs)
peak = np.array(peak)
peak = np.delete(peak, np.where((peak <= 2*fs) | (peak >= winsize-2*fs))[0]).tolist() # 删除中间片段5分钟窗口前2s和最后2sR波位置
peaks.extend(map(lambda n: n+j*winsize-5*fs, peak))
peaks = np.array(peaks)
peaks = np.sort(peaks)
dp = np.abs(np.diff(peaks))
final_peaks = peaks[np.where(dp >= 0.2*fs)[0]+1]
return final_peaks
def sampen(rr_seq, max_temp_len, r):
"""
rr_seq: segment of the RR intervals series
max_temp_len: maximum template length
r: initial value of the tolerance matching
"""
length = len(rr_seq)
lastrun = np.zeros((1,length))
run = np.zeros((1,length))
A = np.zeros((max_temp_len,1))
B = np.zeros((max_temp_len,1))
p = np.zeros((max_temp_len,1))
e = np.zeros((max_temp_len,1))
for i in range(length - 1):
nj = length - i - 1
for jj in range(nj):
j = jj + i + 2
if np.abs(rr_seq[j-1] - rr_seq[i]) < r:
run[0, jj] = lastrun[0, jj] + 1
am1 = float(max_temp_len)
br1 = float(run[0,jj])
M1 = min(am1,br1)
for m in range(int(M1)):
A[m] = A[m] + 1
if j < length:
B[m] = B[m]+1
else:
run[0, jj] = 0
for j in range(nj):
lastrun[0, j] = run[0,j]
N = length * (length - 1) / 2
p[0] = A[0] / N
e[0] = -1 * np.log(p[0] + sys.float_info.min)
for m in range(max_temp_len-1):
p[m+1]=A[m+1]/B[m]
e[m+1]=-1*np.log(p[m+1])
return e, A, B
def comp_cosEn(rr_segment):
r = 0.03 # initial value of the tolerance matching
max_temp_len = 2 # maximum template length
min_num_count = 5 # minimum numerator count
dr = 0.001 # tolerance matching increment
match_num = np.ones((max_temp_len,1)) # number of matches for m=1,2,...,M
match_num = -1000 * match_num
while match_num[max_temp_len-1,0] < min_num_count:
e, match_num, B = sampen(rr_segment, max_temp_len, r)
r = r + dr
if match_num[max_temp_len-1, 0] != -1000:
mRR = np.mean(rr_segment)
cosEn = e[max_temp_len-1, 0] + np.log(2 * (r-dr)) - np.log(mRR)
else:
cosEn = -1000
sentropy = e[max_temp_len-1, 0]
return cosEn, sentropy
def load_dict(filename):
'''load dict from json file'''
with open(filename,"r") as json_file:
dic = json.load(json_file)
return dic
def save_dict(filename, dic):
'''save dict into json file'''
with open(filename,'w') as json_file:
json.dump(dic, json_file, ensure_ascii=False)