def challenge(record, alarm_type):
"""Evaluates the presence of a given alarm in a given record"""
assert alarm_type in (ASYST, BRAD, TACH, VFLUT,
VTACH), ('Unknown alarm type {0}'.format(alarm_type))
annots = MIT.read_annotations(INTERP_DIR + str(record) + '.igqrs')
rec = None
if alarm_type == VTACH:
rec = MIT.load_MIT_record(DB_DIR + str(record))
rec.leads = [VALID_LEAD_NAMES[l] for l in rec.leads]
return EVAL_ALARM_F[alarm_type](annots, rec)
metavar='ann',
required=True,
help=('Annotations resulting from the abductive '
'interpretation of the ECG signal'))
parser.add_argument('-c',
metavar='cluster',
required=True,
help=('Extension of the file containing the clustering'
' information.'))
parser.add_argument('-o',
metavar='oann',
default='cls',
help=('Save annotations with classified QRS complexes'
' as annotator oann (default: cls)'))
args = parser.parse_args()
rec = MIT.load_MIT_record(args.r)
set_sampling_freq(rec.frequency)
print('Classifying record {0}'.format(args.r))
#Reconstruction of the abductive interpretation
annots = MIT.read_annotations('{0}.{1}'.format(args.r, args.a))
interp = interp2annots.ann2interp(rec, annots)
#Cluster information
clusters = load_clustering(args.r, args.c, interp.observations)
#QRS feature extraction
features = get_features(interp)
#Cluster feature extraction
for c in clusters:
clusters[c] = Cluster(clusters[c],
get_cluster_features(clusters[c], features))
#Key function to compare clusters: First, we check clusters with more than
#30 beats; then, the clusters with more REGULAR or AFIB beats, and finally
#DB = '/home/tomas/Escritorio/afdb/'
#DB = '/tmp/mit/'
ANN = '.iqrs'
#ANN = '.ibatr'
OANN = '.rhy'
RECORDS = [l.strip() for l in open(DB + 'RECORDS')]
for rec in RECORDS:
if os.path.isfile(DB+rec+OANN):
print('Annotator "{0}" already exists. Skipping record {1}'.format(
OANN, rec))
continue
print('Converting record {0}'.format(rec))
anns = MIT.read_annotations(DB+rec+ANN)
record = MIT.load_MIT_record(DB+rec)
assert record.frequency == SAMPLING_FREQ
record.leads = [VALID_LEAD_NAMES[l] for l in record.leads]
interp = i2a.ann2interp(record, anns)
afibs = list(interp.get_observations(o.Atrial_Fibrillation))
i = 0
while i < len(afibs):
print('{0}/{1}'.format(i,len(afibs)))
afib = afibs[i]
beats = list(interp.get_observations(o.QRS, filt=lambda q, af=afib:
af.earlystart <= q.time.start <= af.lateend))
rpks = np.array([qrs.time.start for qrs in beats])
#We obtain the shape representing the AFIB morphology as the qrs
#matching the shape with more other qrss within the rhythm.
ctr = collections.Counter()
for qrs in beats: