def run(inp, opt, cfg):
"""
calculate ppv from arterial waveform
:param art: arterial waveform
:return: max, min, upper envelope, lower envelope, respiratory rate, ppv
"""
data = arr.interp_undefined(inp['pleth']['vals'])
srate = inp['pleth']['srate']
data = arr.resample_hz(data, srate, 100)
srate = 100
if len(data) < 30 * srate:
return [{}, {}, {}, {}, {}, [], []]
minlist, maxlist = arr.detect_peaks(data, srate)
maxlist = maxlist[1:]
# estimates the upper ue(n) and lower le(n) envelopes
xa = np.array([data[idx] for idx in minlist])
le = np.array([0] * len(data))
for i in range(len(data)):
be = np.array([b((i - idx) / (0.2 * srate)) for idx in minlist])
s = sum(be)
if s != 0:
le[i] = np.dot(xa, be) / s
xb = np.array([data[idx] for idx in maxlist])
ue = np.array([0] * len(data))
for i in range(len(data)):
be = np.array([b((i - idx) / (0.2 * srate)) for idx in maxlist])
s = sum(be)
if s != 0:
ue[i] = np.dot(xb, be) / s
re = ue - le
re[re < 0] = 0
# estimates resp rate
rr = arr.estimate_resp_rate(re, srate)
# split by respiration
nsamp_in_breath = int(srate * 60 / rr)
m = int(len(data) / nsamp_in_breath) # m segments exist
pps = []
for i in range(m - 1):
imax = arr.max_idx(re, i * nsamp_in_breath, (i+2) * nsamp_in_breath) # 50% overlapping
imin = arr.min_idx(re, i * nsamp_in_breath, (i+2) * nsamp_in_breath)
ppmax = re[imax]
ppmin = re[imin]
ppe = 2 * (ppmax - ppmin) / (ppmax + ppmin) * 100 # estimate
if ppe > 50 or ppe < 0:
continue
pp = cfg['pp']
if pp == 0:
pp = ppe
err = abs(ppe - pp)
if err < 1:
pp = ppe
elif err < 25:
pp = (pp + ppe) / 2
else:
pass # dont update
cfg['pp'] = pp
pps.append({'dt': (i * nsamp_in_breath) / srate, 'val': pp})
return [
[{'dt': cfg['interval'], 'val': rr}],
pps
]
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
data = arr.interp_undefined(inp['pleth']['vals'])
srate = inp['pleth']['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
pulse_vals = []
for i in range(0, len(minlist) - 1):
if not 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
pulse_vals.append({'dt': minlist[i] / srate, 'val': pp})
# estimates the upper env(n) and lower env(n) envelopes
xa = np.array([data[idx] for idx in minlist])
lower_env = np.array([0.0] * len(data))
for i in range(len(data)):
be = np.array([b((i - idx) / (0.2 * srate)) for idx in minlist])
s = sum(be)
if s != 0:
lower_env[i] = np.dot(xa, be) / s
xb = np.array([data[idx] for idx in maxlist])
upper_env = np.array([0.0] * len(data))
for i in range(len(data)):
be = np.array([b((i - idx) / (0.2 * srate)) for idx in maxlist])
s = sum(be)
if s != 0:
upper_env[i] = np.dot(xb, be) / s
pulse_env = upper_env - lower_env
pulse_env[pulse_env < 0.0] = 0.0
# estimates resp rate
rr = arr.estimate_resp_rate(pulse_env, srate)
# split by respiration
nsamp_in_breath = int(srate * 60 / rr)
m = int(len(data) / nsamp_in_breath) # m segments exist
raw_pps = []
pps = []
for ibreath in np.arange(0, m - 1, 0.5):
pps_breath = []
for ppe in pulse_vals:
if ibreath * nsamp_in_breath < ppe['dt'] * srate < (
ibreath + 1) * nsamp_in_breath:
pps_breath.append(ppe['val'])
if len(pps_breath) < 4:
continue
pp_min = min(pps_breath)
pp_max = max(pps_breath)
ppv = 2 * (pp_max - pp_min) / (pp_max + pp_min) * 100 # estimate
if not 0 < ppv < 50:
continue
# raw_pps.append({'dt': (ibreath * nsamp_in_breath) / srate, 'val': pp})
#
# 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 # no update
pps.append({
'dt': ((ibreath + 1) * nsamp_in_breath) / srate,
'val': int(ppv)
})
return [pps, pulse_vals, [{'dt': cfg['interval'], 'val': rr}]]
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
}]]