forked from vitaldb/pyvital
-
Notifications
You must be signed in to change notification settings - Fork 0
/
abp_ppv.py
178 lines (145 loc) · 5.41 KB
/
abp_ppv.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
import arr
import numpy as np
import math
import time
import scipy.interpolate
import scipy.signal
last_ppv = 0
last_spv = 0
cfg = {
'name': 'Pulse Pressure Variation',
'group': 'ABP',
'desc': 'Calculate pulse pressure variation using modified version of the method in the reference',
'reference': 'Aboy et al, An Enhanced Automatic Algorithm for Estimation of Respiratory Variations in Arterial Pulse Pressure During Regions of Abrupt Hemodynamic Changes. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 10, OCTOBER 2009',
'overlap': 20,
'interval': 30, # 30초는 되어야 rr을 추정 가능함
'inputs': [{'name': 'ART', 'type': 'wav'}],
'outputs': [
{'name': 'PPV', 'type': 'num', 'min': 0, 'max': 30, 'unit': '%'},
{'name': 'SPV', 'type': 'num', 'min': 0, 'max': 30, 'unit': '%'},
{'name': 'ART_RR', 'type': 'num', 'min': 0, 'max': 30, 'unit': '/min'}
]
}
def run(inp, opt, cfg):
"""
calculate ppv from arterial waveform
:param art: arterial waveform
:return: max, min, upper envelope, lower envelope, respiratory rate, ppv
"""
global last_ppv, last_spv
data = arr.interp_undefined(inp['ART']['vals'])
srate = inp['ART']['srate']
data = arr.resample_hz(data, srate, 100)
srate = 100
if len(data) < 30 * srate:
print('hr < 30')
return
# beat detection
minlist, maxlist = arr.detect_peaks(data, srate)
maxlist = maxlist[1:]
# beat lengths
beatlens = []
beats_128 = []
beats_128_valid = []
for i in range(0, len(minlist)-1):
beatlen = minlist[i+1] - minlist[i] # in samps
if not 30 < beatlen < 300:
beats_128.append(None)
continue
pp = data[maxlist[i]] - data[minlist[i]] # pulse pressure
if not 20 < pp < 100:
beats_128.append(None)
continue
beatlens.append(beatlen)
beat = data[minlist[i]:minlist[i+1]]
resampled = arr.resample(beat, 128)
beats_128.append(resampled)
beats_128_valid.append(resampled)
if not beats_128_valid:
return
avgbeat = np.array(beats_128_valid).mean(axis=0)
meanlen = np.mean(beatlens)
stdlen = np.std(beatlens)
if stdlen > meanlen * 0.2: # irregular rhythm
return
# remove beats with correlation < 0.9
pp_vals = []
sp_vals = []
for i in range(0, len(minlist)-1):
if beats_128[i] is None or not len(beats_128[i]):
continue
if np.corrcoef(avgbeat, beats_128[i])[0, 1] < 0.9:
continue
pp = data[maxlist[i]] - data[minlist[i]] # pulse pressure
sp = data[maxlist[i]]
pp_vals.append({'dt': minlist[i] / srate, 'val': pp})
sp_vals.append({'dt': minlist[i] / srate, 'val': sp})
dtstart = time.time()
# estimates resp rate
# upper env
idx_start = max(min(minlist),min(maxlist))
idx_end = min(max(minlist),max(maxlist))
xa = scipy.interpolate.CubicSpline(maxlist, [data[idx] for idx in maxlist])(np.arange(idx_start, idx_end))
# lower env
xb = scipy.interpolate.CubicSpline(minlist, [data[idx] for idx in minlist])(np.arange(idx_start, idx_end))
rr = arr.estimate_resp_rate(xa-xb, srate)
dtend = time.time()
#print('rr {}'.format(rr))
# split by respiration
nsamp_in_breath = int(srate * 60 / rr)
m = int(len(data) / nsamp_in_breath) # m segments exist
raw_pps = []
raw_sps = []
ppvs = []
spvs = []
for ibreath in np.arange(0, m - 1, 0.5):
pps_breath = []
sps_breath = []
for ppe in pp_vals:
if ibreath * nsamp_in_breath < ppe['dt'] * srate < (ibreath + 1) * nsamp_in_breath:
pps_breath.append(ppe['val'])
for spe in sp_vals:
if ibreath * nsamp_in_breath < spe['dt'] * srate < (ibreath + 1) * nsamp_in_breath:
sps_breath.append(spe['val'])
if len(pps_breath) < 4:
continue
if len(sps_breath) < 4:
continue
pp_min = min(pps_breath)
pp_max = max(pps_breath)
sp_min = min(sps_breath)
sp_max = max(sps_breath)
ppv = (pp_max - pp_min) / (pp_max + pp_min) * 200
if not 0 < ppv < 50:
continue
spv = (sp_max - sp_min) / (sp_max + sp_min) * 200
if not 0 < spv < 50:
continue
# kalman filter
if last_ppv == 0: # first time
last_ppv = ppv
elif abs(last_ppv - ppv) <= 1.0:
ppv = last_ppv
elif abs(last_ppv - ppv) <= 25.0: # ppv cannot be changed abruptly
ppv = (ppv + last_ppv) * 0.5
last_ppv = ppv
else:
continue
if last_spv == 0: # first time
last_spv = spv
elif abs(last_spv - spv) <= 1.0:
spv = last_spv
elif abs(last_spv - spv) <= 25.0: # ppv cannot be changed abruptly
spv = (spv + last_spv) * 0.5
last_spv = spv
else:
continue
ppvs.append(ppv)
spvs.append(spv)
median_ppv = np.median(ppvs)
median_spv = np.median(spvs)
return [
[{'dt': cfg['interval'], 'val': median_ppv}],
[{'dt': cfg['interval'], 'val': median_spv}],
[{'dt': cfg['interval'], 'val': rr}]
]