def datarecord2(input_directory,target_lead,buf_size=100,segnum=24,seg_length=750, full_seg=True, stt=0):
input_files = []
for f in os.listdir(input_directory):
if os.path.isfile(os.path.join(input_directory, f)) and not f.lower().startswith('.') and f.lower().endswith('mat'):
input_files.append(f)
classes=get_classes(input_directory,input_files)
num_files = len(input_files)
datalabel=[]
SEG_buf = np.zeros([1, seg_length, segnum], dtype=np.float32)
SEGs = np.zeros([1, seg_length, segnum], dtype=np.float32)
for i, f in enumerate(input_files):
print(' {}/{}...'.format(i + 1, num_files))
tmp_input_file = os.path.join(input_directory, f)
data, header_data = load_challenge_data(tmp_input_file)
datalabel.append(getdata_class(header_data))
datalead = data[target_lead,:]
# SEGt = np.float32(utils.sig_process(data, target_length=target_len))
SEGt = utils.Stack_Segs_generate2(datalead, seg_num=segnum, seg_length=seg_length, full_seg=full_seg, stt=stt)
del data,datalead
SEG_buf = np.concatenate((SEG_buf, SEGt))
del SEGt
if SEG_buf.shape[0] >= buf_size:
SEGs = np.concatenate((SEGs, SEG_buf[1:]))
del SEG_buf
SEG_buf = np.zeros([1, seg_length, segnum], dtype=np.float32)
if SEG_buf.shape[0] > 1:
SEGs = np.concatenate((SEGs, SEG_buf[1:]))
del SEG_buf
datalabel = np.array(datalabel)
return SEGs[1:], datalabel,len(classes)
def datarecord(input_directory, downsample, buf_size=100, leadnum=12):
input_files = []
for f in os.listdir(input_directory):
if os.path.isfile(
os.path.join(input_directory, f)
) and not f.lower().startswith('.') and f.lower().endswith('mat'):
input_files.append(f)
classes = get_classes(input_directory, input_files)
num_files = len(input_files)
datalabel = []
target_len = int(72000 / downsample)
SEG_buf = np.zeros([1, target_len, leadnum], dtype=np.float32)
SEGs = np.zeros([1, target_len, leadnum], dtype=np.float32)
for i, f in enumerate(input_files):
print(' {}/{}...'.format(i + 1, num_files))
tmp_input_file = os.path.join(input_directory, f)
data, header_data = load_challenge_data(tmp_input_file)
datalabel.append(getdata_class(header_data))
SEGt = np.float32(utils.sig_process(data, target_length=target_len))
del data
SEG_buf = np.concatenate((SEG_buf, SEGt))
del SEGt
if SEG_buf.shape[0] >= buf_size:
SEGs = np.concatenate((SEGs, SEG_buf[1:]))
del SEG_buf
SEG_buf = np.zeros([1, target_len, leadnum], dtype=np.float32)
if SEG_buf.shape[0] > 1:
SEGs = np.concatenate((SEGs, SEG_buf[1:]))
del SEG_buf
datalabel = np.array(datalabel)
return SEGs[1:], datalabel, len(classes)
def datarecord(input_directory):
input_files = []
for f in os.listdir(input_directory):
if os.path.isfile(os.path.join(input_directory, f)) and not f.lower().startswith('.') and f.lower().endswith('mat'):
input_files.append(f)
num_files = len(input_files)
datalabel=[]
for i, f in enumerate(input_files):
print(' {}/{}...'.format(i + 1, num_files))
tmp_input_file = os.path.join(input_directory, f)
data, header_data = load_challenge_data(tmp_input_file)
datalabel.append(getdata_class(header_data)-1)
datalabel = np.array(datalabel)
return np.array(input_files), datalabel
def datafeatrecord(input_directory,
records,
downsample,
buf_size=100,
leadnum=12,
featurenum=25):
# input_files = []
# for f in os.listdir(input_directory):
# if os.path.isfile(os.path.join(input_directory, f)) and not f.lower().startswith('.') and f.lower().endswith('mat'):
# input_files.append(f)
classes = get_classes(input_directory, records)
num_files = len(records)
datalabel = np.zeros([1, 9])
# label0temp=[]
target_len = int(72000 / downsample)
SEG_buf = np.zeros([1, target_len, leadnum + 1], dtype=np.float32)
SEGs = np.zeros([1, target_len, leadnum + 1], dtype=np.float32)
# feat_buf=np.zeros([1,1,target_len], dtype=np.float32)
featurezero = np.zeros([target_len, 1])
for i, f in enumerate(records):
print(' {}/{}...'.format(i + 1, num_files))
tmp_input_file = os.path.join(input_directory, f)
data, header_data = load_challenge_data(tmp_input_file)
labelonhot, label0 = getdata_class(header_data)
datalabel = np.concatenate((datalabel, labelonhot), axis=0)
# label0temp.append(label0)
features = np.asarray(get_12ECG_features(data, header_data))
featurezero[0:featurenum, 0] = features[0:featurenum]
# feats_reshape = features.reshape(1, -1)
feats_reshape = featurezero.reshape(
[1, featurezero.shape[0], featurezero.shape[1]])
# feat_buf=np.concatenate((feat_buf,feats_reshape))
SEGt = np.float32(utils.sig_process(data, target_length=target_len))
SEGt = np.concatenate((SEGt, feats_reshape), axis=2)
del data
SEG_buf = np.concatenate((SEG_buf, SEGt))
del SEGt
if SEG_buf.shape[0] >= buf_size:
SEGs = np.concatenate((SEGs, SEG_buf[1:]))
del SEG_buf
SEG_buf = np.zeros([1, target_len, leadnum + 1], dtype=np.float32)
if SEG_buf.shape[0] > 1:
SEGs = np.concatenate((SEGs, SEG_buf[1:]))
del SEG_buf
# label0temp = np.array(label0temp)
return SEGs[1:], datalabel[1:]
def datarecord(input_directory):
input_files = []
for f in os.listdir(input_directory):
if os.path.isfile(
os.path.join(input_directory, f)
) and not f.lower().startswith('.') and f.lower().endswith('mat'):
input_files.append(f)
num_files = len(input_files)
datalabel = []
classnamemultemp = []
for i, f in enumerate(input_files):
print(' {}/{}...'.format(i + 1, num_files))
tmp_input_file = os.path.join(input_directory, f)
data, header_data = load_challenge_data(tmp_input_file)
labelonhot, label0 = getdata_class(header_data)
datalabel.append(label0)
# classnamemultemp.append(classnamemul)
datalabel = np.array(datalabel)
# text_save('classname.txt',classnamemultemp)
return np.array(input_files), datalabel
