def test_shhs_200002(self):
path = 'D:\\GitRepositories\\DSDM\\Period1\\github_ecg\\db_shhs\\edfs\\shhs1\\shhs1-200002.edf'
ecg,_,_ = ap.SHHS_ReadDataset(path, 100)
sample_length = 6000
num_samp = int(np.ceil(len(ecg) / sample_length))
result = []
for j in range(num_samp):
l = j * sample_length
h = (j + 1) * sample_length
if j == (num_samp - 1):
break
sample = ecg[l:h]
validity = ap.isValidECGChunk(sample)
result.append(validity)
expected = np.array([True] * num_samp)
invalid_elements = [56, 57, 59, 60, 81, 82, 85, 155, 156, 159, 160, 198, 202, 205, 225, 226, 229, 230, 231, 232]
for i in invalid_elements:
expected[i] = False
expected[232:num_samp-1] = False # rest of array is invalid
for i in range(expected.size):
print (str(i) + ': ' + str(expected[i]))
with open('DetectInvalildSignal_test_shhs_200002.txt', mode='w+',
newline='') as csv_file:
fieldnames = \
['expected',
'result',
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, delimiter='\t')
for i in range(expected.size-1):
writer.writerow({'expected': expected[i],
'result': result[i],
})
number_errors = 0
for i in range(expected.size - 1):
if expected[i] != result[i]:
number_errors += 1
writer.writerow({'expected': 'test_shhs_200002: '+ 'error_num=' + str(number_errors) + ', error[%]='+ str((number_errors/num_samp)*100),
'result': "",
})
self.assertEqual(result, expected)
def test_NoOverlaps_5Hz_To_1Hz_DontPreserveSize(self):
input_apnea_signal = [];
expected_apnea_signal = [];
# create input signal (in 5 element blocks (5Hz) for better visibility)
input_apnea_signal.extend([0, 0, 0, 0, 0]);
input_apnea_signal.extend([1, 0, 0, 0, 0]);
input_apnea_signal.extend([0, 1, 0, 0, 0]);
input_apnea_signal.extend([0, 0, 0, 0, 0]);
input_apnea_signal.extend([0, 0, 1, 0, 0]);
input_apnea_signal.extend([0, 0, 0, 1, 0]);
input_apnea_signal.extend([0, 0, 0, 0, 1]);
input_apnea_signal.extend([0, 0, 0, 0, 0]);
input_apnea_signal.extend([1, 1, 1, 1, 1]);
input_apnea_signal.extend([0, 0, 0, 0, 0]);
input_apnea_signal.extend([0, 1, 1, 1, 0]);
input_apnea_signal.extend([1, 0, 1, 0, 1]);
# create expected signal (in 1 element blocks (1Hz) for better visibility)
expected_apnea_signal.extend([0]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([0]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([0]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([0]);
expected_apnea_signal.extend([1]);
expected_apnea_signal.extend([1]);
output_apnea_signal = ap.ResampleAnnotations(input_apnea_signal, 5, 1, False, False, False);
self.assertEqual(expected_apnea_signal, output_apnea_signal)
def test_NoOverlapInterval_1(self):
input = [0, 1, 1, 1, 0];
expected_output = False;
output = ap.IsFirstApneaOverlapInterval(input);
self.assertEqual(expected_output, output);
def test_OverlapInterval_4(self):
input = [0, 1, 1, 1, 1];
expected_output = True;
output = ap.IsFirstApneaOverlapInterval(input);
self.assertEqual(expected_output, output);
def UCDDB_Functions():
path = 'datasets\\db3_ucddb\\ucddb002_respevt.txt';
apnea_signals = ap.UCDDB_LoadAnnonationsTXTFileRaw(path);
print(apnea_signals);
start_time = datetime.strptime('00:11:04', '%H:%M:%S')
annotations_std = ap.UCDDB_LoadAnnonationsTXTFileStandardized(path, start_time=start_time, duration_in_seconds=7.65*60*60);
print(annotations_std)
annotations_std_binary = [not(type=='none') for type in annotations_std]
resampled = ap.ResampleAnnotations(
annotations=annotations_std_binary,
source_sample_frequency=1,
target_sample_frequency=(1 / 60),
preserve_input_size=False,
ignore_first_timeframe_during_overlap=False,
ignore_short_apnea_in_timeframe=False)
resampled_full_size = ap.ResampleAnnotations(
annotations=annotations_std_binary,
source_sample_frequency=1,
target_sample_frequency=(1 / 60),
preserve_input_size=True,
ignore_first_timeframe_during_overlap=False,
ignore_short_apnea_in_timeframe=False)
resampled_full_size_IgnoreFirstOverlap = ap.ResampleAnnotations(
annotations=annotations_std_binary,
source_sample_frequency=1,
target_sample_frequency=(1 / 60),
preserve_input_size=True,
ignore_first_timeframe_during_overlap=True,
ignore_short_apnea_in_timeframe=False)
plt.plot(resampled)
apn_symbols = list()
for element in resampled:
symbol = 'N'
if element == 1:
symbol = 'A'
apn_symbols.append(symbol)
resampled_1Min = [element*60*128 for element in range(len(apn_symbols))]
print('Write annotation file')
print(resampled_1Min)
print(apn_symbols)
wfdb.wrann('ucddb002', 'apn', np.array(resampled_1Min), np.array(apn_symbols))
with open('datasets\\db3_ucddb\\AnnotationsResampled\\ucddb002_AnnotationsInfo.txt', mode='w', newline='') as csv_file:
fieldnames = \
['Sample',
'DateTime',
'Apnea yes/no',
'Apnea Type',
'Apnea yes/no Resampled',
'Apnea yes/no Resampled Ignore First Overlap'
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, delimiter='\t')
writer.writeheader()
for i in range(len(annotations_std)):
writer.writerow({'Sample': i,
'DateTime': (start_time + timedelta(seconds=i)),
'Apnea yes/no': annotations_std_binary[i],
'Apnea Type': annotations_std[i],
'Apnea yes/no Resampled': resampled_full_size[i],
'Apnea yes/no Resampled Ignore First Overlap': resampled_full_size_IgnoreFirstOverlap[i]
})
return
# target_file_postfix='_Shorter_IgnoreOverlap',
# source_file_portfix='_lifecard_100Hz',
# preserve_input_size=False,
# ignore_first_timeframe_during_overlap=True,
# create_annotationfiles_as_ascii=False,
# print_log=True
#)
#datasets = sorted(glob.glob('D:\\GitRepositories\\DSDM\\Period1\\github_ecg\\db_shhs\\edfs\\shhs1\\*.edf'))
#ap.SHHS_CreateRepresentationChunks(
# paths_datasets=datasets,
# dir_target ='D:\\GitRepositories\\DSDM\\Period1\\github_ecg\\db_shhs\\edfs\\shhs1\\Scalograms',
# database=2,
# target_freq=100,
# sample_seconds=60,
# print_log=True
#)
datasets = sorted(glob.glob('D:\GitRepositories\DSDM\Period1\github_ecg\db3_ucddb\Resampled\Record100Hz\\*.dat'))
ap.SHHS_CreateRepresentationChunks(
paths_datasets=datasets,
dir_target ='D:\GitRepositories\DSDM\Period1\github_ecg\db3_ucddb\Resampled\Record100Hz\\Scalograms',
database=3,
target_freq=100,
sample_seconds=60,
print_log=True
)
#GetApneaOccurencesApneaECG()
# DON'T CHANGE!
target_freq = 100
sample_seconds = 60
datasets = sorted(
glob.glob(
'D:\\GitRepositories\\DSDM\\Period1\\github_ecg\\db_shhs\\edfs\\shhs1\\*.edf'
))
#for i in range(6):
# del datasets[0]
# use if True/False to activate/deactivate the code block without the need to comment/uncomment
if True:
ap.createRepresentationChunks(
paths_datasets=datasets,
dir_target=
'D:\\GitRepositories\\DSDM\\Period1\\github_ecg\\db_shhs\\edfs\\shhs1\\Scalograms1To125_BaselineCorrection',
database=2,
target_freq=target_freq,
sample_seconds=sample_seconds,
create_additional_info=True)
# its assumed that the .dat, .hea and .apn files are in the same directory
# the apnea files have the postfix '_Shorter_IgnoreOverlap'
# f.ex.: ucddb002_lifecard_100Hz.dat, ucddb002_lifecard_100Hz.hea, ucddb002_lifecard_100Hz_Shorter_IgnoreOverlap.apn
if False:
datasets = sorted(
glob.glob(
'D:\GitRepositories\DSDM\Period1\github_ecg\db3_ucddb\Resampled\Record100Hz\\*.dat'
))
ap.createRepresentationChunks(
paths_datasets=datasets,