ncl[1].info.qrs.tag = tag
classified.append(ncl)
clist.remove(ncl)
#Comparative classification
while clist:
c, nxt = clist.pop(0)
tag = comparative_classification(nxt, classified)
for b in nxt.beats:
b.tag = tag
#The cluster representant also is assigned the classification.
nxt.info.qrs.tag = tag
classified.append((c, nxt))
#Afib code is now changed to normality to fit the convention
for _, (beats, _) in classified:
for b in (b for b in beats if isinstance(b, o.QRS)):
if b.tag == AFTAG:
b.tag = C.NORMAL
annots = interp2annots.interp2ann(interp)
#We also include the clustered artifacts.
for b in interp.get_observations(
o.RDeflection,
filt=lambda ba: any([
ba in cl.beats and any(isinstance(b, o.QRS) for b in cl.beats)
for cl in clusters.values()
])):
a = MIT.MITAnnotation.MITAnnotation()
a.code = b.tag
a.time = b.time.start
annots.add(a)
MIT.save_annotations(annots, '{0}.{1}'.format(args.r, args.o))
#The rhythm is assumed to be part of the afib.
afib.end.cpy(nrhythm.end)
interp.observations.remove(nrhythm)
rpks = tmprpks
else:
break
else:
break
else:
break
i += 1
anns = i2a.interp2ann(interp)
for b in interp.get_observations(o.BeatAnn):
a = MIT.MITAnnotation.MITAnnotation()
a.code = MIT.ECGCodes.ARFCT
a.time = b.time.start
anns.add(a)
#Now we select only rhythm annotations and we remove repeated annotations.
anns = [a for a in anns if a.code == COD.RHYTHM]
i = 1
while i < len(anns):
if (anns[i].aux in (b'(N', b'(SVTA', b'(SBR', b'(AFIB', b'(T', b'(B',
b'(VFL')
and anns[i].aux == anns[i-1].aux):
anns.pop(i)
else:
i += 1
MIT.save_annotations(anns, DB+rec+OANN)
del interp
print('All records successfully processed')