def load_IEC(denoise=True, pre=False):
"""
loading IEC CSE dataset and preprocess the signal.
Arg:
pre: (bool) load from saved preprocessed data or not
denoise: (bool) preprocess using wavelet thresholding or not
Returns:
ekg_sig: (Tensor) with sized [#signals, 1 lead, signal_length]
"""
# (num of ekg signal, length, 1)
if pre:
ekg_sig = torch.load(config["RetinaNet"]["output_path"] +
"IEC_preprocessed_data.pt").to('cuda')
else:
ekg_sig = []
for i in range(1, 126):
ekg_filename = config["General"]["IEC_path"] + 'CSE' + str(
i).rjust(3, '0') + '.raw'
try:
sig = read_IEC(ekg_filename)
sig = np.reshape(sig[0], (len(sig[0]), 1))
ekg_sig.append(sig.astype(float))
except IOError:
print("file {} does not exist".format("CSE" +
str(i).rjust(3, '0')))
ekg_sig = IEC_dataset_preprocessing(ekg_sig, smooth=False, dns=denoise)
ekg_sig = ekg_sig.to('cuda')
ekg_sig = normalize(ekg_sig, instance=True)
torch.save(
ekg_sig,
config["RetinaNet"]["output_path"] + "IEC_preprocessed_data.pt")
return ekg_sig
def load_raw_dataset_and_bbox_labels_CAL():
"""
Get raw dataset with signals, bboxes and labels
Args:
raw_dataset: (string) dataset path
Returns:
X: (Array) input signals with sized [#subjects, #leads, signal_length=5000]
BBoxes: (list) target bboxes with sized [#subjects, #leads, #objs, 2]
Labels: (list) target labels with sized [#subjects, #leads, #objs]
"""
name = [
"ANE20000", "ANE20001", "ANE20002", "CAL05000", "CAL10000", "CAL15000",
"CAL20000", "CAL20002", "CAL20100", "CAL20110", "CAL20160", "CAL20200",
"CAL20210", "CAL20260", "CAL20500", "CAL30000", "CAL50000"
]
path = config["General"]["IEC_path"]
X = []
BBoxes = []
Labels = []
Peaks = []
for i in range(len(name)):
x = []
bbox = []
label = []
peak = []
for j in range(5):
signal = list(read_IEC(path + name[i] + "_" + str(j + 1) + ".raw"))
with open(path + "CAL_label/" + name[i] + "_" + str(j + 1) +
".json") as f:
label_ = json.load(f)
p_delin = label_['p']
qrs_delin = label_['qrs']
t_delin = label_['t']
# background label 0 will add when encoding
p_boxes, p_labels, p_peaks = get_bbox_labels(p_delin, 0)
qrs_boxes, qrs_labels, qrs_peaks = get_bbox_labels(qrs_delin, 1)
t_boxes, t_labels, t_peaks = get_bbox_labels(t_delin, 2)
b = [*p_boxes, *qrs_boxes, *t_boxes]
l = [*p_labels, *qrs_labels, *t_labels]
p = [*p_peaks, *qrs_peaks, *t_peaks]
x.append(signal[0][start_point:end_point])
bbox.append(b)
label.append(l)
peak.append(p)
X.append(x)
BBoxes.append(bbox)
Labels.append(label)
Peaks.append(peak)
return X, BBoxes, Labels, Peaks
def test(net, signal_file_path):
"""
test the signal using retinanet and return all of the predict result
Args:
net: (nn.Module) RetinaNet module
signal_file_path: (string) this file must be .raw format
Returns:
intervals: (list) p duration, pq interval, qrs duration, qt interval mean and var value
qrs_intervals: (list) q duration, r duration, and s duration mean value
"""
"""load signal file from .raw"""
raw_ekg_sig = []
try:
sig = read_IEC(signal_file_path)
sig = np.reshape(sig[0], (len(sig[0]), 1))
raw_ekg_sig.append(sig.astype(float))
except IOError:
print(f"file {signal_file_path} does not exist.")
ekg_sig = IEC_dataset_preprocessing(raw_ekg_sig, smooth=False,
dns=False).to('cuda')
denoise_sig = IEC_dataset_preprocessing(raw_ekg_sig,
smooth=False,
dns=True).to('cuda')
"""predict the pqrst segmentation result"""
final_intervals = []
final_preds = []
denoise_sig = normalize(denoise_sig)
for i in range(denoise_sig.size(0) // 128 + 1):
plots, intervals, pred_signals = test_retinanet(
net, denoise_sig[i * 128:(i + 1) * 128, :, :], 4992, visual=False)
print(intervals)
final_intervals.extend(intervals)
final_preds.append(pred_signals)
final_preds = torch.cat(final_preds, dim=0)
pred = qrs_seperation(ekg_sig, final_preds)
for i in range(len(pred)):
pred[i]["q_duration"] = np.mean(pred[i]["q_duration"])
pred[i]["r_duration"] = np.mean(pred[i]["r_duration"])
pred[i]["s_duration"] = np.mean(pred[i]["s_duration"])
print(pred)
return final_intervals, pred
def load_ANE_CAL(denoise=True, pre=False, save=True, nor=True):
"""
loading IEC ANE and CAL dataset and preprocess the signal
Arg:
denoise: (bool) denoise using wavelet threshold or not
pre: (bool) load from saved preprocessed data or not
save: (bool) save preprocessed data to file or not
normalize: (bool) normalize the signal or not
Return:
ekg_sig: (Tensor) with shape [#signals, 1 lead, signal_length]
"""
name = json.loads(config["General"]["CAL_name"])
# (num of ekg signal, length, 1)
if pre and os.path.isfile(config["RetinaNet"]["output_path"] +
"CAL_preprocessed_data.pt"):
ekg_sig = torch.load(config["RetinaNet"]["output_path"] +
"CAL_preprocessed_data.pt").to('cuda')
if nor:
ekg_sig = normalize(ekg_sig, instance=True)
else:
ekg_sig = []
for i in range(len(name)):
for j in range(1, 6):
ekg_filename = f'{config["General"]["IEC_path"]}{name[i]}_{str(j)}.raw'
try:
sig = read_IEC(ekg_filename)
sig = np.reshape(sig[0], (len(sig[0]), 1))
ekg_sig.append(sig.astype(float))
except IOError:
print(f"file {name[i]}_{str(j)} does not exist")
ekg_sig = IEC_dataset_preprocessing(ekg_sig, smooth=False, dns=denoise)
ekg_sig = ekg_sig.to('cuda')
if nor:
ekg_sig = normalize(ekg_sig, instance=True)
if save:
torch.save(
ekg_sig, config["RetinaNet"]["output_path"] +
"CAL_preprocessed_data.pt")
return ekg_sig