def findUniformDecelCandidates(
sig,
mask,
sigSmoothed,
baseline,
ts,
threshBase=-1,
minChange=5,
threshDeriv=1.0, # 0.5
threshDeriv2=0.125, # 0.25
):
deriv = np.diff(sigSmoothed)
delta = sigSmoothed - baseline
allMin, allMax = getCrossings(deriv)
allPoints = [[i, 'max'] for i in allMax if delta[i] > threshBase]
allPoints += [[i, 'min'] for i in allMin if delta[i] <= threshBase]
_, allEnd = getCrossings(deriv - threshDeriv)
allPoints += [[i, 'end'] for i in allEnd if delta[i] >= -minChange]
_, allStart = getCrossings(deriv + threshDeriv)
allPoints += [[i, 'start'] for i in allStart if delta[i] >= -minChange]
if threshDeriv2:
_, allEnd = getCrossings(deriv - threshDeriv2)
allPoints += [[i, 'end'] for i in allEnd if delta[i] >= -minChange]
_, allStart = getCrossings(deriv + threshDeriv2)
allPoints += [[i, 'start'] for i in allStart if delta[i] >= -minChange]
allPoints = sorted(allPoints)
allPoints = pruneMinima(allPoints, sigSmoothed)
allPoints = pruneEndpoints(allPoints, sigSmoothed)
allDecels = identifyUniformDecel(allPoints, sigSmoothed, baseline, ts,
mask)
return allPoints, allDecels
def findUC(sigUC, ts, minSpacing=10.0 / 8, verbose=False):
idxMinima, idxMaxima = getCrossings(np.diff(sigUC))
peaks = sorted([[sigUC[i], i] for i in idxMaxima], reverse=True)
peaks = [i for _, i in peaks]
selected = []
for i, idxThis in enumerate(peaks):
adjacent = [
True for idxOther in peaks[:i]
if abs(ts[idxThis] - ts[idxOther]) < minSpacing
]
if not adjacent:
selected.append(idxThis)
selected = sorted(selected)
if verbose:
for i in selected:
print '{:0.1f}: {:0.1f}'.format(ts[i], sigUC[i])
return selected, [ts[i] for i in selected]
def findVarDecelCandidates(sig,
mask,
sigSmoothed,
baseline,
ts,
nadirWidth=5,
includeEnd=True):
"""Find potential variable decelerations """
delta = sig - baseline
deltaSmoothed5 = sigSmoothed - baseline + 5
allRelease, allOnset = getCrossings(deltaSmoothed5)
allEndpoints = [[i, 'onset'] for i in allOnset] + [[i, 'release']
for i in allRelease]
allEndpoints = sorted(allEndpoints)
if includeEnd and allEndpoints and allEndpoints[-1] != 'release':
# create release at end of recording
allEndpoints.append([len(sig) - 1, 'release'])
results = []
for i, entry in enumerate(allEndpoints):
if entry[1] != 'release':
continue
if i == 0 or allEndpoints[i - 1][1] != 'onset':
continue
idxStart = allEndpoints[i - 1][0]
idxEnd = entry[0]
duration = (ts[idxEnd] - ts[idxStart]) * 60
if duration < 10:
continue
# refine onset/release estimates using local slops
slopeL = (sigSmoothed[idxStart] - sigSmoothed[idxStart + 5]) / 5.0
slopeR = (sigSmoothed[idxEnd] - sigSmoothed[idxEnd - 5]) / 5.0
if slopeL > 0:
idxStart = max(idxStart - int(5.0 / slopeL), 0)
if slopeR > 0:
idxEnd = min(idxEnd + int(5.0 / slopeR), len(sig) - 1)
startingBaseline = baseline[idxStart]
# estimate magnitude based on triangle
auc = np.sum(-delta[idxStart:idxEnd])
triangleRatio = 2 * auc / (idxEnd - idxStart)
pctValid = np.mean(mask[idxStart:idxEnd])
idxNadir = np.argmin(sigSmoothed[idxStart:idxEnd]) + idxStart
# find actual
idxMin = np.argmin(sig[idxNadir - nadirWidth:idxNadir +
nadirWidth]) + idxNadir - nadirWidth
# idxMin = np.argmin(sig[idxStart:idxEnd]) + idxStart
relMag = -delta[idxMin]
mag = sig[idxMin]
duration = (ts[idxEnd] - ts[idxStart]) * 60
tOnset = (ts[idxNadir] - ts[idxStart]) * 60
tRelease = (ts[idxEnd] - ts[idxNadir]) * 60
decel = {
'relMag': relMag,
'mag': mag,
'pctValid': pctValid,
'base': startingBaseline,
'duration': duration,
'tOnset': tOnset,
'tRelease': tRelease,
'idxNadir': idxNadir,
'idxStart': idxStart,
'idxEnd': idxEnd,
'tNadir': float(ts[idxNadir]),
'tStart': float(ts[idxStart]),
'tEnd': float(ts[idxEnd]),
'triangleRatio': triangleRatio
}
results.append(decel)
return results
def findProlongedDecelCandidates(sig,
mask,
sigSmoothed,
baseline,
ts,
var,
minRelMag=15,
minDuration=(2.5 * 60)):
"""Find potential prolonged decelerations"""
delta = sig - baseline
deltaSmoothed5 = sigSmoothed - baseline + 5
allRelease, allOnset = getCrossings(deltaSmoothed5)
results = []
lastEnd = -1
for idxStart in allOnset:
if idxStart < lastEnd: # overlap
continue
# use starting point as baseline
i = np.argmax(sigSmoothed[idxStart:] > sigSmoothed[idxStart])
if i == 0:
idxEnd = len(sigSmoothed) - 1
else:
idxEnd = idxStart + i
lastEnd = idxEnd
duration = (ts[idxEnd] - ts[idxStart]) * 60
if duration < minDuration:
continue
pctValid = np.mean(mask[idxStart:idxEnd])
idxNadir = np.argmin(sigSmoothed[idxStart:idxEnd]) + idxStart
startingBaseline = baseline[idxStart]
mag = sigSmoothed[idxNadir]
relMag = startingBaseline - mag
if relMag < minRelMag:
continue
# Time under 15 bpm
deltaT = ts[1] - ts[0]
sigSmoothed[idxStart:idxEnd] < startingBaseline - 15
time_under_15 = np.sum(
sigSmoothed[idxStart:idxEnd] < startingBaseline - 15) * deltaT
# time under 50% of relMag
sigSmoothed[idxStart:idxEnd] < startingBaseline - 15
time_under_50pct = np.sum(
sigSmoothed[idxStart:idxEnd] < startingBaseline -
0.5 * relMag) * deltaT
duration = (ts[idxEnd] - ts[idxStart]) * 60
tOnset = (ts[idxNadir] - ts[idxStart]) * 60
tRelease = (ts[idxEnd] - ts[idxNadir]) * 60
segVar = computeSegmentVariability(idxStart, idxEnd, var, mask)
decel = {
'relMag': relMag,
'mag': mag,
'pctValid': pctValid,
'base': startingBaseline,
'duration': duration,
'tOnset': tOnset,
'tRelease': tRelease,
'idxNadir': idxNadir,
'idxStart': idxStart,
'idxEnd': idxEnd,
'tNadir': float(ts[idxNadir]),
'tStart': float(ts[idxStart]),
'tEnd': float(ts[idxEnd]),
'var': segVar,
'time_under_15': time_under_15,
'time_under_50pct': time_under_50pct
}
results.append(decel)
return results
def findAccelCandidates(sig,
mask,
sigSmoothed,
baseline,
ts,
classifiers=[],
peakWidth=5,
includeEnd=True,
verbose=True):
"""Find accelerations"""
delta = sig - baseline
deltaSmoothed5 = sigSmoothed - baseline - 5
allRelease, allOnset = getCrossings(-deltaSmoothed5)
allEndpoints = [[i, 'onset'] for i in allOnset] + [[i, 'release']
for i in allRelease]
allEndpoints = sorted(allEndpoints)
if includeEnd and allEndpoints and allEndpoints[-1] != 'release':
# create release at end of recording
allEndpoints.append([len(sig) - 1, 'release'])
results = []
for i, entry in enumerate(allEndpoints):
if entry[1] != 'release':
continue
if i == 0 or allEndpoints[i - 1][1] != 'onset':
continue
idxStart = allEndpoints[i - 1][0]
idxEnd = entry[0]
duration = (ts[idxEnd] - ts[idxStart]) * 60
if duration < 10:
continue
pctValid = np.mean(mask[idxStart:idxEnd])
idxPeak = np.argmax(sigSmoothed[idxStart:idxEnd]) + idxStart
# find actual
idxPeak = np.argmax(
sig[idxPeak - peakWidth:idxPeak + peakWidth]) + idxPeak - peakWidth
# idxMin = np.argmin(sig[idxStart:idxEnd]) + idxStart
relMag = delta[idxPeak]
mag = sig[idxPeak]
duration = (ts[idxEnd] - ts[idxStart]) * 60
tOnset = (ts[idxPeak] - ts[idxStart]) * 60
tRelease = (ts[idxEnd] - ts[idxPeak]) * 60
decel = {
'relMag': relMag,
'mag': mag,
'pctValid': pctValid,
'duration': duration,
'tOnset': tOnset,
'tRelease': tRelease,
'idxNadir': idxPeak,
'idxStart': idxStart,
'idxEnd': idxEnd,
'tNadir': float(ts[idxPeak]),
'tStart': float(ts[idxStart]),
'tEnd': float(ts[idxEnd]),
'triangleRatio': None
}
results.append(decel)
return results