def detext_xqrs(datas, disp_err=False) -> list:
if type(datas) == 'list':
xqrs = wfp.XQRS(np.array(datas), fs=250)
else:
xqrs = wfp.XQRS(datas, fs=250)
xqrs.detect(verbose=False)
if len(xqrs.qrs_inds) == 0:
print("QRS Detect failed. return 75")
# index = int(xqrs.qrs_inds[0])
return xqrs.qrs_inds
def extractRpeaks(rdnames, rpeak_lead, samplefrom=0, sampleto='end', verbose=False):
allsymbols = []
for rdname in rdnames:
print(rdname)
sig, fields = wfdb.rdsamp('mitdb/'+rdname, channels='all', sampfrom=samplefrom, sampto=sampleto)
ann_ref = wfdb.rdann('mitdb/'+rdname,'atr', sampfrom=samplefrom, sampto=None if sampleto=='end' else sampleto)
peak_channel = 0
if rpeak_lead in fields['sig_name']:
peak_channel = fields['sig_name'].index(rpeak_lead)
else:
continue
xqrs = processing.XQRS(sig=sig[:,peak_channel], fs=fields['fs'])
xqrs.detect()
acts = xqrs.qrs_inds
comparitor = processing.compare_annotations(ref_sample=ann_ref.sample[1:],
test_sample=xqrs.qrs_inds,
window_width=int(0.1 * fields['fs']),
signal=sig[:,peak_channel])
if verbose:
comparitor.print_summary()
matched_inds = comparitor.matching_sample_nums
outpath = 'mitdb/'+rdname + '.Rpeaks'
with open(outpath, 'wb') as handle:
pickle.dump({'acts':acts, 'matched_inds':matched_inds, 'anns': ann_ref.symbol[1:]}, handle)
def main():
FILE_DIR = "../../rawData/MIT/"
heartbeats_full = []
labels_full = []
for filename in os.listdir(FILE_DIR):
if filename.endswith(".dat"):
fn = FILE_DIR + filename[:-4]
print(fn)
sample, metadata = wfdb.rdsamp(fn)
annotation = wfdb.rdann(fn, 'atr')
lead1, lead2 = zip(*sample)
xqrs = wdpc.XQRS(sig=np.asarray(lead1), fs=360)
xqrs.detect()
heartbeats, labels = split_samples_by_annotation(
sample, annotation, xqrs.qrs_inds)
heartbeats_full += heartbeats
labels_full += labels
print(np.shape(heartbeats_full))
print(np.shape(labels_full))
print("Saving Data...")
np.save("../../processedData/MIT/heartbeats.npy", heartbeats_full)
np.save("../../processedData/MIT/labels.npy", labels_full)
print("Saved")
def load_data(data_path):
files = glob.glob(os.path.join(data_path, "*/*.dat"))
x_total = []
y_total = []
for f_name in tqdm.tqdm(files):
p_idx = int(f_name.split("/")[2].split("_")[-1])-1
signals, _ = wfdb.rdsamp(os.path.join(os.path.dirname(f_name), os.path.basename(f_name).split(".")[0]))
ecg = sosfiltfilt(sos, signals[:,1])
# R-peak detection
xqrs = processing.XQRS(sig=ecg, fs=FS)
xqrs.detect(verbose = False)
# Amplitude normalization
ecg = (ecg - ecg.mean()) / ecg.std()
# R-peak based Beat segmentation
r_peak_locs = xqrs.qrs_inds
seg_start_idx = 0
if not r_peak_locs.size == 0:
for idx in range(len(r_peak_locs)-1):
if idx != 0:
seg_start_idx = seg_end_idx
seg_end_idx = int((r_peak_locs[idx] + r_peak_locs[idx+1]) / 2)
ecg_segment = resample(ecg[seg_start_idx:seg_end_idx], STEP)
x_total.append(ecg_segment)
y_total.append(p_idx)
return x_total, y_total
def detext_xqrs(datas, disp_err=False) -> int:
xqrs = wfp.XQRS(np.array(datas), fs=250)
xqrs.detect(verbose=False)
if len(xqrs.qrs_inds) == 0:
if disp_err:
ax: Axes = plt.subplot()
ax.plot(datas)
plt.show()
print("QRS Detect failed. return 75")
return 75
index = int(xqrs.qrs_inds[0])
return index
def test_plot(self):
record_name = self.mitdb + "100"
sig, fields = wfdb.rdsamp(record_name, channels=[0])
print(sig.__class__, sig)
print(fields.__class__, fields)
ann_ref = wfdb.rdann(record_name, 'atr')
xqrs = processing.XQRS(sig=sig[:, 0], fs=fields['fs'])
print(xqrs)
xqrs.detect()
comparitor = processing.compare_annotations(ann_ref.sample[1:],
xqrs.qrs_inds,
int(0.1 * fields['fs']),
sig[:, 0])
comparitor.print_summary()
comparitor.plot()
def test_xqrs(self):
"""
Run xqrs detector on record 100 and compare to reference annotations
"""
sig, fields = wfdb.rdsamp('sample-data/100', channels=[0])
ann_ref = wfdb.rdann('sample-data/100','atr')
xqrs = processing.XQRS(sig=sig[:,0], fs=fields['fs'])
xqrs.detect()
comparitor = processing.compare_annotations(ann_ref.sample[1:],
xqrs.qrs_inds,
int(0.1 * fields['fs']))
assert comparitor.sensitivity > 0.99
assert comparitor.positive_predictivity > 0.99
def getXQRS(signal, fields):
'''
The qrs detector class for the xqrs algorithm
Args:
signal (list): y values of signal samples
fields (list): properties of signal
Returns:
xqrs (XQRS object): used to plot signals
'''
# QRS Detection
xqrs = processing.XQRS(sig=signal[:, 0], fs=fields['fs'])
xqrs.detect(verbose=True)
return xqrs
def main():
FILE_DIR = "data/"
normal_final = []
abnormal_final = []
abnormal_label_final = []
for filename in os.listdir(FILE_DIR):
if filename.endswith(".dat"):
fn = FILE_DIR + filename[:-4]
print(fn)
sample, _ = wfdb.rdsamp(fn)
annotation = wfdb.rdann(fn, 'atr')
sample_array, _ = zip(*sample)
xqrs = wdpc.XQRS(sig=np.asarray(sample_array), fs=360)
xqrs.detect()
n_s, a_s, a_l = split_samples_by_annotation(sample_array, annotation, xqrs.qrs_inds)
print(len(n_s))
print(len(a_s))
for sig in n_s:
normal_final.append(sig)
for asig in a_s:
abnormal_final.append(asig)
for label in a_l:
abnormal_label_final.append(label)
print(np.shape(normal_final))
print(np.shape(abnormal_final))
print(np.shape(abnormal_label_final))
with open('normal.pickle', 'wb') as norm_file:
pickle.dump(normal_final, norm_file)
with open('abnormal.pickle', 'wb') as abnorm_file:
pickle.dump(abnormal_final, abnorm_file)
with open('abnormal_label.pickle', 'wb') as abnorm_label_file:
pickle.dump(abnormal_label_final, abnorm_label_file)
def SC_method_QRS(signal: NDArray[float], conf, fs=100) -> (List, List, List):
xqrs = processing.XQRS(sig=signal, fs=fs, conf=conf)
xqrs.detect(verbose=False)
correct_peaks = xqrs.qrs_inds
if len(correct_peaks) > 1:
durlist = [
correct_peaks[i + 1] - correct_peaks[i]
for i in range(len(correct_peaks) - 1)
]
ptplist = [
np.ptp(signal[correct_peaks[i]:correct_peaks[i + 1]])
for i in range(len(correct_peaks) - 1)
]
else:
ptplist = [0]
durlist = [0]
return correct_peaks, ptplist, durlist
import matplotlib.pyplot as plt
import wfdb
from wfdb import processing
sig, fields = wfdb.rdsamp('100', channels=[0], sampto=15000, pb_dir='mitdb/')
ann_ref = wfdb.rdann('100', 'atr', sampto=15000, pb_dir='mitdb/')
#使用XQRS算法
xqrs = processing.XQRS(sig=sig[:, 0], fs=fields['fs'])
xqrs.detect()
#这里还可以直接使用xqrs_detection
#qrs_inds=processing.xqrs_detect(sig=sig[:,0], fs=fields['fs'])
#下面进行算法的结果和注释中的结果相对比
#注意：在100.atr中的qrs注释第一个数是18，而18这个位置上并没有峰值，真正的第一个峰值是在第二个数77开始的所以是[1:]
comparitor = processing.compare_annotations(ref_sample=ann_ref.sample[1:],
test_sample=xqrs.qrs_inds,
window_width=int(0.1 *
fields['fs']),
signal=sig[:, 0])
#输出结果
comparitor.print_summary()
fig = comparitor.plot(title='XQRS detected QRS vs reference annotations',
return_fig=True)
# display(fig[0])
plt.show(fig[0]) #这一步必须加，不然图片会一闪而逝
def test_detect(self):
record_name = self.mitdb + "100"
sig, fields = wfdb.rdsamp(record_name, channels=[0])
ann_ref = wfdb.rdann(record_name, 'atr')
xqrs = processing.XQRS(sig=sig[:, 0], fs=fields['fs'])
xqrs.detect()
