def _load_test_data_from_file(self, patient, filename, preprocess=True):
"""
Loading single file test data
:param patient:
:param filename:
:return:
"""
assert ( filename.find('test'))
print "\nLoading test data for " + patient + filename
eeg_data_tmp = EEGData(filename)
eeg_data = eeg_data_tmp.get_instances()
assert len(eeg_data) == 1
eeg_data = eeg_data[0]
fs = eeg_data.sample_rate
# preprocessing
data = eeg_data.eeg_data
params = self.params
params['fs']=fs
### comment if no preprocessing
if preprocess==True:
eeg_data.eeg_data = preprocessing.preprocess_multichannel_data(data,params)
x = self.feature_extractor.extract(eeg_data)
self.features_test.append(np.hstack(x))
def load_train_data_from_file(patient_name, filename, params=None):
"""
Loading single file training data
filename: full path to .mat file
return: (EEG data Instance, y_seizure, y_early)
"""
#print "\nLoading train data for " + patient_name + filename
eeg_data_tmp = EEGData(filename)
# a list of Instance's
eeg_data = eeg_data_tmp.get_instances()
assert len(eeg_data) == 1
eeg_data = eeg_data[0]
# eeg_data is now an Instance
# determine labels based on filename
if filename.find('interictal') > -1:
y_seizure = 0
y_early = 0
elif eeg_data.latency < 15:
y_seizure = 1
y_early = 1
else:
y_seizure = 1
y_early = 0
fs = eeg_data.sample_rate
# preprocessing
data = eeg_data.eeg_data
params['fs'] = fs
eeg_data.eeg_data = preprocessing.preprocess_multichannel_data(
data, params)
return (eeg_data, y_seizure, y_early)
def _load_test_data_from_file(self, patient, filename, preprocess=True):
"""
Loading single file test data
:param patient:
:param filename:
:return:
"""
assert (filename.find('test'))
print("\nLoading test data for " + patient + filename)
eeg_data_tmp = EEGData(filename)
eeg_data = eeg_data_tmp.get_instances()
assert len(eeg_data) == 1
eeg_data = eeg_data[0]
fs = eeg_data.sample_rate
# preprocessing
data = eeg_data.eeg_data
params = self.params
params['fs'] = fs
### comment if no preprocessing
if preprocess == True:
eeg_data.eeg_data = preprocessing.preprocess_multichannel_data(
data, params)
x = self.feature_extractor.extract(eeg_data)
self.features_test.append(np.hstack(x))
def _load_data_from_file(self, filename):
eeg = EEGData(filename)
instances = eeg.get_instances()
filename2 = filename.replace('interictal', 'ictal')
eeg2 = EEGData(filename2)
instances2 = eeg2.get_instances()
feature_vectors = []
for instance in instances:
new_features = self._get_feature_vector_from_instance(instance)
feature_vectors.append(new_features)
for instance in instances2:
new_features = self._get_feature_vector_from_instance(instance)
feature_vectors.append(new_features)
self.episode_matrices.append(self._merge_vectors_into_matrix(feature_vectors))
# self.type_labels.append(np.ones(len(feature_vectors), dtype=np.int8) * eeg.label)
eeg_labels = np.ones(len(instances), dtype=np.int8) * eeg.label
eeg2_labels = np.ones(len(instances2), dtype=np.int8) * eeg2.label
self.type_labels.append(np.concatenate((eeg_labels, eeg2_labels)))
# Only have 1 if there is a seizure and it is early
# self.early_labels.append(np.array(map(self._is_early, instances), dtype=np.int8) * eeg.label)
early_labels = np.array(map(self._is_early, instances), dtype=np.int8) * eeg.label
early2_labels = np.array(map(self._is_early, instances2), dtype=np.int8) * eeg2.label
self.early_labels.append(np.concatenate((early_labels, early2_labels)))
def _load_training_data_from_file(self,
patient,
filename,
preprocess=True):
"""
Loading single file training data
:param patient:
:param filename:
:return:
"""
#print "\nLoading train data for " + patient + filename
eeg_data_tmp = EEGData(filename)
# a list of Instance's
eeg_data = eeg_data_tmp.get_instances()
assert len(eeg_data) == 1
# eeg_data is now an Instance
eeg_data = eeg_data[0]
if filename.find('interictal') > -1:
y_seizure = 0
y_early = 0
elif eeg_data.latency < 15:
y_seizure = 1
y_early = 1
else:
y_seizure = 1
y_early = 0
fs = eeg_data.sample_rate
# preprocessing
data = eeg_data.eeg_data
params = self.params
params['fs'] = fs
### comment if no preprocessing
if preprocess == True:
eeg_data.eeg_data = preprocessing.preprocess_multichannel_data(
data, params)
###
x = self.feature_extractor.extract(eeg_data)
self.features_train.append(np.hstack(x))
self.type_labels.append(y_seizure)
self.early_labels.append(y_early)
def load_test_data_from_file(patient_name, filename, params=None):
"""
Loading single file test data
:return: EEG data Instance (no labels returned)
"""
assert (filename.find('test'))
#print "\nLoading test data for " + patient_name + filename
eeg_data_tmp = EEGData(filename)
eeg_data = eeg_data_tmp.get_instances()
assert len(eeg_data) == 1
# an Instance
eeg_data = eeg_data[0]
fs = eeg_data.sample_rate
data = eeg_data.eeg_data
params['fs'] = fs
eeg_data.eeg_data = preprocessing.preprocess_multichannel_data(
data, params)
return eeg_data
def _load_training_data_from_file(self, patient, filename, preprocess=True):
"""
Loading single file training data
:param patient:
:param filename:
:return:
"""
#print "\nLoading train data for " + patient + filename
eeg_data_tmp = EEGData(filename)
# a list of Instance's
eeg_data = eeg_data_tmp.get_instances()
assert len(eeg_data) == 1
# eeg_data is now an Instance
eeg_data = eeg_data[0]
if filename.find('interictal') > -1:
y_seizure=0
y_early=0
elif eeg_data.latency < 15:
y_seizure=1
y_early=1
else:
y_seizure=1
y_early=0
fs = eeg_data.sample_rate
# preprocessing
data = eeg_data.eeg_data
params = self.params
params['fs']=fs
### comment if no preprocessing
if preprocess==True:
eeg_data.eeg_data = preprocessing.preprocess_multichannel_data(data,params)
###
x = self.feature_extractor.extract(eeg_data)
self.features_train.append(np.hstack(x))
self.type_labels.append(y_seizure)
self.early_labels.append(y_early)
def _load_data_from_file(self, filename):
eeg = EEGData(filename)
instances = eeg.get_instances()
filename2 = filename.replace('interictal', 'ictal')
eeg2 = EEGData(filename2)
instances2 = eeg2.get_instances()
feature_vectors = []
for instance in instances:
new_features = self._get_feature_vector_from_instance(instance)
feature_vectors.append(new_features)
for instance in instances2:
new_features = self._get_feature_vector_from_instance(instance)
feature_vectors.append(new_features)
self.episode_matrices.append(self._merge_vectors_into_matrix(feature_vectors))
# self.type_labels.append(np.ones(len(feature_vectors), dtype=np.int8) * eeg.label)
eeg_labels = np.ones(len(instances), dtype=np.int8) * eeg.label
eeg2_labels = np.ones(len(instances2), dtype=np.int8) * eeg2.label
self.type_labels.append(np.concatenate((eeg_labels, eeg2_labels)))
# Only have 1 if there is a seizure and it is early
# self.early_labels.append(np.array(map(self._is_early, instances), dtype=np.int8) * eeg.label)
early_labels = np.array(list(map(self._is_early, instances)), dtype=np.int8) * eeg.label
early2_labels = np.array(list(map(self._is_early, instances2)), dtype=np.int8) * eeg2.label
self.early_labels.append(np.concatenate((early_labels, early2_labels)))