def Test1():
'''Comparing to expert labels in QTdb.'''
qt = QTloader()
reclist = qt.getreclist()
rec_ind = 0
for rec_ind in xrange(0, len(reclist)):
print 'Processing record[%d] %s ...' % (rec_ind, reclist[rec_ind])
sig = qt.load(reclist[rec_ind])
raw_sig = sig['sig']
expert_labels = qt.getExpert(reclist[rec_ind])
R_pos_list = [
x[0] for x in filter(lambda item: item[1] == 'R', expert_labels)
]
# Skip empty expert lists
if len(R_pos_list) == 0:
continue
dpi = DPI()
qrs_list = dpi.QRS_Detection(raw_sig)
# Find FN
FN_arr = GetFN(R_pos_list, qrs_list)
R_pos_list = FN_arr
if len(R_pos_list) > 0:
plt.plot(raw_sig)
amp_list = [raw_sig[x] for x in qrs_list]
plt.plot(qrs_list, amp_list, 'ro', markersize=12)
amp_list = [raw_sig[x] for x in R_pos_list]
plt.plot(R_pos_list, amp_list, 'ys', markersize=14)
plt.show()
def Test_hog1d():
'''Hog feature method test.'''
target_label = 'T'
qt = QTloader()
rec_ind = 103
reclist = qt.getreclist()
sig = qt.load(reclist[rec_ind])
raw_sig = sig['sig']
len_sig = len(raw_sig)
# Hog feature tester
hog = HogFeatureExtractor(target_label=target_label)
rec_list = hog.qt.getreclist()
training_list = reclist[0:100]
# training_rec = list(set(rec_list) - set(testing_rec))
hog.Train(training_list)
# Load the trained model
# with open('./hog.mdl', 'rb') as fin:
# mdl = pickle.load(fin)
# hog.LoadModel(mdl)
# Save the trained model
with open('./hog.mdl', 'wb') as fout:
pickle.dump(hog.gbdt, fout)
dpi = DPI()
qrs_list = dpi.QRS_Detection(raw_sig)
start_time = time.time()
detected_poslist = hog.Testing(raw_sig,
zip(qrs_list, [
'R',
] * len(qrs_list)))
print 'Testing time: %d s.' % (time.time() - start_time)
while len(detected_poslist) > 0 and detected_poslist[-1] > len_sig:
del detected_poslist[-1]
while len(detected_poslist) > 0 and detected_poslist[0] < 0:
del detected_poslist[0]
sig = qt.load(reclist[rec_ind])
raw_sig = sig['sig']
plt.plot(raw_sig, label='raw signal D1')
plt.plot(sig['sig2'], label='raw signal D2')
amp_list = [raw_sig[int(x)] for x in detected_poslist]
plt.plot(detected_poslist,
amp_list,
'ro',
markersize=12,
label=target_label)
plt.title('Record name %s' % reclist[rec_ind])
plt.legend()
plt.show()
def TestMit():
'''Comparing to expert labels in Mitdb.'''
mit = MITdbLoader()
reclist = mit.getRecIDList()
print dir(mit)
rec_ind = 0
for rec_ind in xrange(3, len(reclist)):
print 'Processing record[%d] %s ...' % (rec_ind, reclist[rec_ind])
sig = mit.load(reclist[rec_ind])
raw_sig = sig
# expert_labels = mit.getExpert(reclist[rec_ind])
R_pos_list = [int(round(x)) for x in mit.markpos]
# plt.plot(sig)
# amp_list = [sig[int(x)] for x in R_pos_list]
# plt.plot(R_pos_list, amp_list, 'ro', markersize = 12)
# plt.show()
# Skip empty expert lists
if len(R_pos_list) == 0:
continue
debug_info = dict()
debug_info['time_cost'] = 75410
# debug_info['decision_plot'] = 57181
dpi = DPI(debug_info=debug_info)
qrs_list = dpi.QRS_Detection(raw_sig, fs=360)
# Find FN
FN_arr = GetFN(R_pos_list, qrs_list)
R_pos_list = FN_arr
if len(R_pos_list) > 0:
plt.plot(raw_sig)
amp_list = [raw_sig[x] for x in qrs_list]
plt.plot(qrs_list,
amp_list,
'ro',
markersize=12,
label='Detected R with DPI')
amp_list = [raw_sig[x] for x in R_pos_list]
plt.plot(R_pos_list,
amp_list,
'ys',
markersize=14,
label='Expert labels')
plt.legend()
plt.title('Record %s' % reclist[rec_ind])
plt.show()
def Test_regression():
'''Regression test.'''
target_label = 'T'
qt = QTloader()
rec_ind = 65
reclist = qt.getreclist()
sig = qt.load(reclist[rec_ind])
raw_sig = sig['sig']
len_sig = len(raw_sig)
rf = RegressionLearner(target_label=target_label)
rf.TrainQtRecords(reclist[0:])
# Load the trained model
# with open('./tmp.mdl', 'rb') as fin:
# mdl = pickle.load(fin)
# rf.LoadModel(mdl)
# Save the trained model
with open('./tmp.mdl', 'wb') as fout:
pickle.dump(rf.mdl, fout)
dpi = DPI()
qrs_list = dpi.QRS_Detection(raw_sig)
start_time = time.time()
detected_poslist = rf.testing(raw_sig,
zip(qrs_list, [
'R',
] * len(qrs_list)))
print 'Testing time: %d s.' % (time.time() - start_time)
while len(detected_poslist) > 0 and detected_poslist[-1] > len_sig:
del detected_poslist[-1]
sig = qt.load(reclist[rec_ind])
raw_sig = sig['sig']
plt.plot(raw_sig, label='raw signal')
amp_list = [raw_sig[int(x)] for x in detected_poslist]
plt.plot(detected_poslist,
amp_list,
'ro',
markersize=12,
label=target_label)
plt.legend()
plt.show()
def testing(self, raw_sig, fs=250.0):
'''Testing API.
Returns:
A list of (index, label) pairs. For example:
[(1, 'R'), (24, 'T'), (35, 'Toffset'),]
'''
if isinstance(raw_sig, list):
len_sig = len(raw_sig)
elif isinstance(raw_sig, np.ndarray):
len_sig = raw_sig.size
detected_results = list()
# Detect R first
debug_info = dict()
debug_info['time_cost'] = True
dpi = DPI(debug_info=debug_info)
qrs_list = dpi.QRS_Detection(raw_sig, fs=fs)
detected_results.extend(zip(qrs_list, [
'R',
] * len(qrs_list)))
for target_label in ['T', 'Toffset', 'Ponset', 'P', 'Poffset']:
start_time = time.time()
detected_poslist = self.model_dict[target_label].Testing(
raw_sig, detected_results)
print 'Testing time: %d s.' % (time.time() - start_time)
while len(detected_poslist) > 0 and detected_poslist[-1] > len_sig:
del detected_poslist[-1]
while len(detected_poslist) > 0 and detected_poslist[0] < 0:
del detected_poslist[0]
detected_results.extend(
zip(detected_poslist, [
target_label,
] * len(detected_poslist)))
return detected_results
def TestZN():
with open('./data.pkl', 'rb') as fin:
data = pickle.load(fin)
sig = data.tolist()
raw_sig = np.squeeze(sig['II'])
raw_sig = [x / 100.0 for x in raw_sig]
debug_info = dict()
debug_info['time_cost'] = 75410
debug_info['decision_plot'] = 3517
dpi = DPI(debug_info=debug_info)
qrs_list = dpi.QRS_Detection(raw_sig, fs=500.0)
# pdb.set_trace()
plt.plot(raw_sig)
amp_list = [raw_sig[x] for x in qrs_list]
plt.plot(qrs_list,
amp_list,
'ro',
markersize=12,
label='Detected R with DPI')
plt.legend()
plt.show()
def Test_Mit():
'''Hog feature method test.'''
target_label = 'T'
# Hog feature tester
hog = HogFeatureExtractor(target_label=target_label)
rec_list = hog.qt.getreclist()
training_list = rec_list[0:]
# training_rec = list(set(rec_list) - set(testing_rec))
# hog.Train(training_list)
# Load the trained model
with open('./hog.mdl', 'rb') as fin:
mdl = pickle.load(fin)
hog.LoadModel(mdl)
# Save the trained model
# with open('./hog.mdl', 'wb') as fout:
# pickle.dump(hog.gbdt, fout)
# Testing on mit database
mit = MITdbLoader()
rec_name = mit.getreclist()[9]
raw_sig = mit.load(rec_name)
resample_length = int(len(raw_sig) * 250.0 / 360.0)
raw_sig = scipy.signal.resample(raw_sig, resample_length)
len_sig = len(raw_sig)
debug_info = dict()
debug_info['time_cost'] = True
dpi = DPI(debug_info=debug_info)
qrs_list = dpi.QRS_Detection(raw_sig)
start_time = time.time()
detected_poslist = hog.Testing(raw_sig,
zip(qrs_list, [
'R',
] * len(qrs_list)))
print 'Testing time: %d s.' % (time.time() - start_time)
while len(detected_poslist) > 0 and detected_poslist[-1] > len_sig:
del detected_poslist[-1]
while len(detected_poslist) > 0 and detected_poslist[0] < 0:
del detected_poslist[0]
raw_sig = mit.load(rec_name)
raw_sig = scipy.signal.resample(raw_sig, resample_length)
sigd2 = scipy.signal.resample(mit.sigd2, resample_length)
plt.plot(raw_sig, label='raw signal D1')
plt.plot(sigd2, label='raw signal D2')
amp_list = [raw_sig[int(x)] for x in detected_poslist]
plt.plot(detected_poslist,
amp_list,
'ro',
markersize=12,
label=target_label)
plt.title('Record name %s' % rec_name)
plt.legend()
plt.show()