async def score_record(self, record, annotation):
"""Obtain detector performance for an annotated record.
Parameters
----------
record : 1d array
The raw physiological record.
annotation : 1d array
The manual extrema annotations.
Returns
-------
precision : float
The detectors precision on the record given the tolerance.
sensitivity : float
The detectors sensitivity on the record given the tolerance.
"""
detector_annotation = self.detector(record, self.sfreq)
comparitor = compare_annotations(detector_annotation, annotation,
self.tolerance)
tp = comparitor.tp
fp = comparitor.fp
fn = comparitor.fn
sensitivity = tp / (tp + fn)
precision = tp / (tp + fp)
return precision, sensitivity
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 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 calc_ECG_annotation(annotation_GT, reconstructed, fs=360):
qrs_inds = processing.xqrs_detect(sig=reconstructed, fs=fs, verbose=True)
# Compare detected qrs complexes to reference annotation.
# Note, first sample in 100.atr is not a qrs.ֶ
comparitor = processing.compare_annotations(ref_sample=annotation_GT,
test_sample=qrs_inds,
window_width=int(0.1 * fs),
signal=reconstructed)
F_mesure = 2 * (comparitor.positive_predictivity *
comparitor.sensitivity) / (comparitor.positive_predictivity
+ comparitor.sensitivity)
return [
len(annotation_GT), comparitor.n_test, comparitor.tp, comparitor.fp,
comparitor.fn, comparitor.positive_predictivity,
comparitor.sensitivity, F_mesure
]
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.specificity > 0.99
assert comparitor.positive_predictivity > 0.99
assert comparitor.false_positive_rate < 0.01
def benchmark_record(record, sampling_rate, annotation, tolerance, detector):
"""Obtain detector performance for an annotated record.
Parameters
----------
record : array
The raw physiological record.
sampling_rate: int
The sampling rate of the record in Hertz.
annotation : array
The manual extrema annotations.
tolerance : int
Maximum difference in millisecond that is permitted between the manual
annotation and the annotation generated by the detector.
detector : function
A function that takes a physiological record as first positional
argument as well as a `sampling_rate` keyword argument.
Returns
-------
precision : float
The detectors precision on the record given the tolerance.
sensitivity : float
The detectors sensitivity on the record given the tolerance.
"""
detector_annotation = detector(record, sampling_rate=sampling_rate)
comparitor = compare_annotations(detector_annotation, annotation, tolerance)
tp = comparitor.tp
fp = comparitor.fp
fn = comparitor.fn
sensitivity = tp / (tp + fn)
precision = tp / (tp + fp)
return precision, sensitivity
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
ecg = np.ravel(pd.read_csv(record, sep=" ", usecols=[1],
header=None))
except FileNotFoundError:
print(f"no ECG available for {record}")
continue
try:
manupeaks = np.ravel(pd.read_csv(annotation, header=None))
except FileNotFoundError:
print(f"no annotations available for {annotation}")
continue
algopeaks = ecg_peaks(ecg, sfreq)
if algopeaks.size > 1:
comparitor = compare_annotations(manupeaks, algopeaks, tolerance)
tp = comparitor.tp
fp = comparitor.fp
fn = comparitor.fn
# plt.figure()
# plt.plot(ecg)
# plt.scatter(manupeaks, ecg[manupeaks], c="m")
# plt.scatter(algopeaks, ecg[algopeaks], c='g', marker='X', s=150)
sensitivity.append(float(tp) / (tp + fn))
precision.append(float(tp) / (tp + fp))
print(f"sensitivity = {sensitivity[-1]}, precision = {precision[-1]}")
print(f"mean precision = {np.mean(precision)}, std precision = {np.std(precision)}")
print(f"mean sensitivity = {np.mean(sensitivity)}, std sensitivity = {np.std(sensitivity)}")
tolerance = int(np.rint(.05 * sfreq)) # tolerance must be in samples for wfdb
print(
f"Setting tolerance for match between algorithmic and manual annotation"
f" to {tolerance} samples, corresponding to 50 milliseconds at a sampling rate of {sfreq}."
)
sensitivity = []
precision = []
for subject in subjects:
data = np.loadtxt(os.path.join(record_dir, subject[0]))
annotation = np.loadtxt(os.path.join(annotation_dir, subject[1]))
peaks = ppg_peaks(data, sfreq)
comparitor = compare_annotations(peaks, annotation, tolerance)
tp = comparitor.tp
fp = comparitor.fp
fn = comparitor.fn
sensitivity.append(float(tp) / (tp + fn))
precision.append(float(tp) / (tp + fp))
print(f"sensitivity = {sensitivity[-1]}, precision = {precision[-1]}")
print(
f"mean precision = {np.mean(precision)}, std precision = {np.std(precision)}"
)
print(
f"mean sensitivity = {np.mean(sensitivity)}, std sensitivity = {np.std(sensitivity)}"
)
f"Setting tolerance for match between algorithmic and manual annotation"
f" to {tolerance} sample(s), corresponding to {tolerance / sfreq} seconds"
f" at a sampling rate of {sfreq}.")
sensitivity = []
precision = []
for subject in data_dir.iterdir():
f = h5py.File(subject, "r")
record = np.ravel(f["signal"]["pleth"]["y"])
annotation = np.ravel(f["labels"]["pleth"]["peak"]["x"])
peaks = ppg_peaks(record, sfreq)
comparitor = compare_annotations(annotation, peaks, tolerance)
tp = comparitor.tp
fp = comparitor.fp
fn = comparitor.fn
sensitivity.append(tp / (tp + fn))
precision.append(tp / (tp + fp))
print(f"\nResults {subject}")
print("-" * len(str(subject)))
print(f"sensitivity = {sensitivity[-1]}")
print(f"precision = {precision[-1]}")
print(f"\nAverage results over {len(precision)} records")
print("-" * 31)
print(
