def fix_labels(signals, beats, labels):
"""
Change labeling of the normal beats.
Beat index of some normal beats doesn't occur at the local maxima
of the ECG signal in MIT-BIH Arrhytmia database. Function checks if
beat index occurs within 5 samples from the local maxima. If this is
not true, beat labeling is changed to -1.
Parameters
----------
signals : list
List of ECG signals as numpy arrays
beats : list
List of numpy arrays that store beat locations
labels : list
List of numpy arrays that store beat types
Returns
-------
fixed_labels : list
List of numpy arrays where -1 has been added for beats that are
not located in local maxima
"""
fixed_labels = []
for s, b, l in zip(signals, beats, labels):
# Find local maximas
localmax = find_local_peaks(sig=s, radius=5)
localmax = correct_peaks(sig=s,
peak_inds=localmax,
search_radius=5,
smooth_window_size=20,
peak_dir='up')
# Make sure that beat is also in local maxima
fixed_p = correct_peaks(sig=s,
peak_inds=b,
search_radius=5,
smooth_window_size=20,
peak_dir='up')
# Check what beats are in local maximas
beat_is_local_peak = np.isin(fixed_p, localmax)
fixed_l = l
# Add -1 if beat is not in local max
fixed_l[~beat_is_local_peak] = -1
fixed_labels.append(fixed_l)
return fixed_labels
def get_rr_peaks_indices(record, max_bpm=230):
"""
:param record_name:
:param database:
:param max_bpm:
:return: list of timestamps for
"""
qrs_inds = processing.gqrs_detect(sig=record.p_signal[:, 0],
fs=record.fs)
search_radius = int(record.fs * 60 / max_bpm)
corrected_peak_inds = processing.correct_peaks(
record.p_signal[:, 0], peak_inds=qrs_inds,
search_radius=search_radius, smooth_window_size=150)
result = []
discarded_count = 0
prev_pi = -1
for pi in corrected_peak_inds:
if pi != prev_pi:
result.append(pi)
else:
discarded_count += 1
prev_pi = pi
# returns r-r peaks timestamps
result = np.array(result) / record.fs
return result, discarded_count
def SC_method_localpeak(signal: NDArray[float],
peak_window: int = 50,
mean_average_window: int = 30,
direction: str = 'down') -> (List, List, List):
hard_peaks = processing.find_local_peaks(signal, peak_window)
correct_peaks = processing.correct_peaks(signal,
hard_peaks,
peak_window,
mean_average_window,
peak_dir=direction)
correct_peaks = np.unique(correct_peaks)
correct_peaks = correct_peaks[(correct_peaks > -1)
& (correct_peaks < len(signal))]
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
def test_correct_peaks(self):
sig, fields = wfdb.rdsamp('sample-data/100')
ann = wfdb.rdann('sample-data/100', 'atr')
fs = fields['fs']
min_bpm = 10
max_bpm = 350
min_gap = fs * 60 / min_bpm
max_gap = fs * 60 / max_bpm
y_idxs = processing.correct_peaks(sig=sig[:, 0],
peak_inds=ann.sample,
search_radius=int(max_gap),
smooth_window_size=150)
yz = np.zeros(sig.shape[0])
yz[y_idxs] = 1
yz = np.where(yz[:10000] == 1)[0]
expected_peaks = [
77, 370, 663, 947, 1231, 1515, 1809, 2045, 2403, 2706, 2998, 3283,
3560, 3863, 4171, 4466, 4765, 5061, 5347, 5634, 5919, 6215, 6527,
6824, 7106, 7393, 7670, 7953, 8246, 8539, 8837, 9142, 9432, 9710,
9998
]
assert np.array_equal(yz, expected_peaks)
def get_peaks(raw_signal: np.ndarray, fs: int) -> np.ndarray:
MAX_BPM = 220
raw_peaks, _ = find_peaks(raw_signal,
distance=int((60 / MAX_BPM) / (1 / fs)))
med_peaks = processing.correct_peaks(raw_signal,
raw_peaks,
30,
35,
peak_dir='up')
# print("med_peaks: ", med_peaks[:10])
# print("med_peaks: ", med_peaks[:10])
# print("med_peaks: ", med_peaks[:10])
try:
wel_peaks = processing.correct_peaks(
raw_signal, med_peaks, 30, 35,
peak_dir='up') if len(med_peaks) > 0 else raw_peaks
except ValueError:
return med_peaks[~np.isnan(med_peaks)]
return wel_peaks[~np.isnan(wel_peaks)]
def get_qrs_inds(self, signals, fs):
qrs_inds = processing.correct_peaks(
signals[:, 0],
processing.xqrs_detect(signals[:, 0],
fs,
conf=processing.XQRS.Conf(hr_min=20,
hr_max=230,
qrs_width=0.5)),
fs * 60 // 230, fs // 2, 'compare')
return qrs_inds
def test_correct_peaks(self):
sig, fields = wfdb.rdsamp('sample-data/100')
ann = wfdb.rdann('sample-data/100', 'atr')
fs = fields['fs']
min_bpm = 10
max_bpm = 350
min_gap = fs*60/min_bpm
max_gap = fs * 60 / max_bpm
y_idxs = processing.correct_peaks(sig=sig[:,0], peak_inds=ann.sample,
search_radius=int(max_gap),
smooth_window_size=150)
yz = np.zeros(sig.shape[0])
yz[y_idxs] = 1
yz = np.where(yz[:10000]==1)[0]
expected_peaks = [77, 370, 663, 947, 1231, 1515, 1809, 2045, 2403,
2706, 2998, 3283, 3560, 3863, 4171, 4466, 4765, 5061,
5347, 5634, 5919, 6215, 6527, 6824, 7106, 7393, 7670,
7953, 8246, 8539, 8837, 9142, 9432, 9710, 9998]
assert np.array_equal(yz, expected_peaks)
def detect_qrs(ecg_measurements, fs, win_len_min=10, win_overlap_min=1):
win_len = int(fs * 60 * win_len_min)
win_overlap = int(fs * 60 * win_overlap_min)
indices = set()
win_skip = win_len - win_overlap
wins = int((len(ecg_measurements) - win_len) / win_skip) + 2
min_bpm = 20
max_bpm = 230
search_radius = int(fs * 60 / max_bpm)
start_time_all = time.time()
for i, w_start in enumerate([x * win_skip for x in range(wins)]):
w_end = w_start + win_len
start_time = time.time()
calc_data = ecg_measurements[w_start:w_end]
qrs_inds = processing.xqrs_detect(sig=calc_data,
fs=fs,
learn=True,
verbose=False)
corrected_peak_inds = processing.correct_peaks(
calc_data,
peak_inds=qrs_inds,
search_radius=search_radius,
smooth_window_size=150)
start_upd_dict_time = time.time()
corrected_peak_inds = corrected_peak_inds + w_start
indices.update(corrected_peak_inds)
end_time = time.time()
print(
"{:5} {:10} - {:10} czas wykonania: {:10.2} całkowity czas wykonania: {:10.2} liczba probek: {} liczba qrs: {} liczba probek w zbiorze {}"
.format(i, w_start, w_end, end_time - start_time,
end_time - start_time_all, len(calc_data),
len(corrected_peak_inds), len(indices)),
flush=True)
result = np.array(list(indices))
np.ndarray.sort(result)
return result
def eval_model(test_exs,
eval_fun,
params,
plot_examples=True,
exs=None,
nb=2,
threshold=None,
nearest_fpr=None,
eval_margin=10):
assert threshold is not None or nearest_fpr is not None
min_gap = params['min_gap']
max_gap = params['max_gap']
left_border = params['left_border']
right_border = params['right_border']
fs_target = params['fs_target']
segment_size = params['segment_size']
segment_step = params['segment_step']
normalize_steps = params['normalize_steps']
smooth_window_correct = params['smooth_window_correct']
if exs is None:
exs = numpy.random.randint(len(test_exs), size=nb).tolist()
if plot_examples:
fig, ax = plt.subplots(len(exs),
figsize=(30, 10 * len(exs))) #, dpi=600)
y_trues = []
y_preds = []
print('Evaluating', end='')
for i, ex_id in enumerate(exs):
print('.', end='')
#print('Example {}'.format(ex_id))
db, k, j = test_exs[ex_id]
XY = load_steps(db, k, params)[j]
X, Y = numpy.reshape(XY[0],
(1, 1, 5000)), numpy.reshape(XY[1], (5000, ))
#print(X.shape, Y.shape)
res = eval_fun(X)
res = res[0][0]
if threshold is not None:
x = numpy.where(res >= threshold)
numpy.put(res, x, 1.0)
res = res.astype('int32')
best_peaks_idxs = correct_peaks(x=X[0][0],
peak_indexes=res,
min_gap=min_gap,
max_gap=max_gap,
smooth_window=smooth_window_correct)
best_peaks_vals = X[0][0][best_peaks_idxs]
y_true = Y
y_pred = numpy.zeros(len(res))
y_pred[best_peaks_idxs] = 1
y_trues += y_true.tolist()[left_border:-right_border]
y_preds += res.tolist()[left_border:-right_border]
rp, fp, tp, fpr, tpr, thresholds, auc = roc_auc(
y_true[left_border:-right_border],
y_pred[left_border:-right_border],
margin=eval_margin)
if plot_examples:
ax[i].plot(Y + 1, color='blue')
ax[i].plot(X[0][0], color='green')
ax[i].plot(res - 1, color='red')
b_peaks_idx = numpy.where(Y == 1)[0]
ax[i].plot(b_peaks_idx, X[0][0][b_peaks_idx], 'b+')
ax[i].plot(best_peaks_idxs, best_peaks_vals, 'r+')
ax[i].plot([left_border, left_border], [-1, 2], 'm-')
ax[i].plot([len(res) - right_border,
len(res) - right_border], [-1, 2], 'm-')
ax[i].set_title(
'Example {} ({}/{}/{}) (TP={}/{}, FP={}/0, TPR={}, FPR={})'.
format(ex_id, db, k, j, tp, rp, fp, tpr, fpr))
print()
if plot_examples:
plt.show()
rp, fp, tp, fpr, tpr, thresholds, auc = roc_auc(numpy.asarray(y_trues),
numpy.asarray(y_preds),
margin=eval_margin)
print('FPR\t\t\tTPR\t\t\tThreshold')
if nearest_fpr is not None:
idx = (numpy.abs(fpr - nearest_fpr)).argmin()
print('{:.6f}\t\t{:.6f}\+t\t{:.7f}'.format(fpr[idx], tpr[idx],
thresholds[idx]))
else:
for i, t in enumerate(thresholds):
print('{:.6f}\t\t{:.6f}\+t\t{:.7f}'.format(fpr[i], tpr[i], t))
plot_roc(fpr, tpr, auc, figsize=(10, 10))
print('Samples:\t\t{} samples'.format(len(y_trues)))
print('Beats:')
print(' - {} labelized'.format(rp))
print(' - {} detected'.format(fp + tp))
print(' - TP: {}/{}'.format(tp, rp))
print(' - FP: {}/{}'.format(fp, 0))
print(' - TPR: {:.4f}'.format(tp / rp))
if saveto is not None:
plt.savefig(saveto, dpi=600)
plt.show()
#加载ECG信号
record=wfdb.rdrecord('./2') #.hea .dat文件名称
#help(wfdb.rdrecord)
#使用gqrs算法定位qrs波位置
qrs_inds=processing.gqrs_detect(sig=record.p_signal[:, 0], fs=record.fs) #未矫正位置
#画出结果
#peaks_hr(sig=record.p_signal, peak_inds=qrs_inds, fs=record.fs, title='GQRS peak detection on record 100')
#修正峰值，将其设置为局部最大值
min_bpm=20
max_bpm=230
#使用可能最大的bpm作为搜索半径
search_radius=int(record.fs*60/max_bpm)
corrected_peak_inds=processing.correct_peaks(record.p_signal[:, 0], peak_inds=qrs_inds, search_radius=search_radius, smooth_window_size=150)
#输出矫正后的QRS波峰位置
print('Corrected gqrs detected peak indices:', sorted(corrected_peak_inds))
# Feature 1: 计算R波波峰
signal=record.p_signal
R_peak = -100
for x in corrected_peak_inds:
if R_peak < max(signal[x]):
R_peak = max(signal[x])
print(R_peak)
def dataGeneration(data_path, csv_path, record_path):
# initialize dataset
dataset = pd.DataFrame(columns=['label', 'record'])
if record_path == None:
# a loop for each patient
detail_path = data_path + '/'
record_files = [
i.split('.')[0] for i in os.listdir(detail_path)
if (not i.startswith('.') and i.endswith('.hea'))
]
Bar.check_tty = False
bar = Bar('Processing',
max=len(record_files),
fill='#',
suffix='%(percent)d%%')
# a loop for each record
for record_name in record_files:
# load record
signal, info = wfdb.rdsamp(detail_path + record_name)
fs = 200
signal = processing.resample_sig(signal[:, 0], info['fs'], fs)[0]
# set some parameters
window_size_half = int(fs * 0.125 / 2)
max_bpm = 230
# detect QRS peaks
qrs_inds = processing.gqrs_detect(signal, fs=fs)
search_radius = int(fs * 60 / max_bpm)
corrected_qrs_inds = processing.correct_peaks(
signal,
peak_inds=qrs_inds,
search_radius=search_radius,
smooth_window_size=150)
average_qrs = 0
count = 0
for i in range(1, len(corrected_qrs_inds) - 1):
start_ind = corrected_qrs_inds[i] - window_size_half
end_ind = corrected_qrs_inds[i] + window_size_half + 1
if start_ind < corrected_qrs_inds[
i - 1] or end_ind > corrected_qrs_inds[i + 1]:
continue
average_qrs = average_qrs + signal[start_ind:end_ind]
count = count + 1
# remove outliers
if count < 8:
print('\noutlier detected, discard ' + record_name)
continue
average_qrs = average_qrs / count
corrcoefs = []
for i in range(1, len(corrected_qrs_inds) - 1):
start_ind = corrected_qrs_inds[i] - window_size_half
end_ind = corrected_qrs_inds[i] + window_size_half + 1
if start_ind < corrected_qrs_inds[
i - 1] or end_ind > corrected_qrs_inds[i + 1]:
corrcoefs.append(-100)
continue
corrcoef = pearsonr(signal[start_ind:end_ind], average_qrs)[0]
corrcoefs.append(corrcoef)
max_corr = list(map(corrcoefs.index, heapq.nlargest(8, corrcoefs)))
index_corr = random.sample(
list(itertools.permutations(max_corr, 8)), 100)
for index in index_corr:
# a temp dataframe to store one record
record_temp = pd.DataFrame()
signal_temp = []
for i in index:
start_ind = corrected_qrs_inds[i + 1] - window_size_half
end_ind = corrected_qrs_inds[i + 1] + window_size_half + 1
sig = processing.normalize_bound(signal[start_ind:end_ind],
-1, 1)
signal_temp = np.concatenate((signal_temp, sig))
record_temp = record_temp.append(pd.DataFrame(
signal_temp.reshape(-1, signal_temp.shape[0])),
ignore_index=True,
sort=False)
record_temp['label'] = record_name
record_temp['record'] = record_name
# add it to final dataset
dataset = dataset.append(record_temp,
ignore_index=True,
sort=False)
bar.next()
bar.finish()
else:
patient_folders = [
i for i in os.listdir(data_path)
if (not i.startswith('.') and i.startswith(record_path))
]
Bar.check_tty = False
bar = Bar('Processing',
max=len(patient_folders),
fill='#',
suffix='%(percent)d%%')
# a loop for each patient
for patient_name in patient_folders:
detail_path = data_path + patient_name + '/'
record_files = [
i.split('.')[0] for i in os.listdir(detail_path)
if i.endswith('.hea')
]
# a loop for each record
for record_name in record_files:
# load record
signal, info = wfdb.rdsamp(detail_path + record_name)
fs = 200
signal = processing.resample_sig(signal[:, 0], info['fs'],
fs)[0]
# set some parameters
window_size_half = int(fs * 0.125 / 2)
max_bpm = 230
# detect QRS peaks
qrs_inds = processing.gqrs_detect(signal, fs=fs)
search_radius = int(fs * 60 / max_bpm)
corrected_qrs_inds = processing.correct_peaks(
signal,
peak_inds=qrs_inds,
search_radius=search_radius,
smooth_window_size=150)
average_qrs = 0
count = 0
for i in range(1, len(corrected_qrs_inds) - 1):
start_ind = corrected_qrs_inds[i] - window_size_half
end_ind = corrected_qrs_inds[i] + window_size_half + 1
if start_ind < corrected_qrs_inds[
i - 1] or end_ind > corrected_qrs_inds[i + 1]:
continue
average_qrs = average_qrs + signal[start_ind:end_ind]
count = count + 1
# remove outliers
if count < 8:
print('\noutlier detected, discard ' + record_name +
' of ' + patient_name)
continue
average_qrs = average_qrs / count
corrcoefs = []
for i in range(1, len(corrected_qrs_inds) - 1):
start_ind = corrected_qrs_inds[i] - window_size_half
end_ind = corrected_qrs_inds[i] + window_size_half + 1
if start_ind < corrected_qrs_inds[
i - 1] or end_ind > corrected_qrs_inds[i + 1]:
corrcoefs.append(-100)
continue
corrcoef = pearsonr(signal[start_ind:end_ind],
average_qrs)[0]
corrcoefs.append(corrcoef)
max_corr = list(
map(corrcoefs.index, heapq.nlargest(8, corrcoefs)))
index_corr = random.sample(
list(itertools.permutations(max_corr, 8)), 100)
for index in index_corr:
# a temp dataframe to store one record
record_temp = pd.DataFrame()
signal_temp = []
for i in index:
start_ind = corrected_qrs_inds[i +
1] - window_size_half
end_ind = corrected_qrs_inds[i +
1] + window_size_half + 1
sig = processing.normalize_bound(
signal[start_ind:end_ind], -1, 1)
signal_temp = np.concatenate((signal_temp, sig))
record_temp = record_temp.append(pd.DataFrame(
signal_temp.reshape(-1, signal_temp.shape[0])),
ignore_index=True,
sort=False)
record_temp['label'] = patient_name
record_temp['record'] = record_name
# add it to final dataset
dataset = dataset.append(record_temp,
ignore_index=True,
sort=False)
bar.next()
bar.finish()
# save for further use
dataset.to_csv(csv_path, index=False)
print('processing completed')
def find_peaks(self, signal, verbose=False):
"""
Execute the peak detection algorithm.
Function uses LSTM model that was trained with 1000 sample windows
of simulated noisy ECG data with sampling rate of 250 Hz. Following
steps are executed:
1. Input ECG is sig to 250 Hz
2. Input ECG is divided into overlapping 1000 sample windows
3. LSTM model is used to make predictions for windows from step 2
4. R-peak locations are decided based on predictions
5. R-peak locations are mapped back into original sampling frequency
and they are corrected upwards
Parameters
----------
signal : array
Single channel ECG signal.
verbose : bool
Whether print information.
Returns
-------
orig_peaks : array
Indices of the R-peaks as samples from the beginning from the
original signal (not resampled signal).
filtered_proba : array
Probability values (probability that point is an R-peak) for
all points in orig_peaks array.
"""
if self.resample:
if verbose:
print("Resampling signal from ", self.iput_fs, "Hz to 250 Hz")
sig = resample_poly(signal, up=250, down=self.iput_fs)
else:
sig = signal
if verbose:
print("Extracting windows, window size:", self.win_size,
" stride:", self.stride)
padded_indices, data_windows = self._extract_windows(signal=sig)
# Normalize each window to -1, 1 range
normalize = partial(processing.normalize_bound, lb=-1, ub=1)
data_windows = np.apply_along_axis(normalize, 1, data_windows)
if verbose:
print("Predicting peaks")
predictions = self.model.predict(data_windows, verbose=0)
if verbose:
print("Calculating means for overlapping predictions (windows)")
means_for_predictions = self._mean_preds(win_idx=padded_indices,
preds=predictions,
orig_len=sig.shape[0])
predictions = means_for_predictions
if verbose:
print("Filtering out predictions below probabilty threshold ",
self.threshold)
filtered_peaks, filtered_proba = self._filter_predictions(
signal=sig, preds=predictions)
if self.resample:
# Resampled positions
resampled_pos = np.round(np.linspace(
0, (sig.shape[0] - 0.5),
int(sig.shape[0] * (self.iput_fs / 250))),
decimals=1)
# Resample peaks back to original frequency
orig_peaks = processing.resample_ann(resampled_pos, filtered_peaks)
# Correct peaks with respect to original signal
orig_peaks = processing.correct_peaks(sig=signal,
peak_inds=orig_peaks,
search_radius=int(
self.iput_fs / 50),
smooth_window_size=20,
peak_dir='up')
# In some very rare cases final correction can introduce duplicate
# peak values. If this is the case, then mean of the duplicate
# values is taken.
filtered_proba = self._calculate_means(indices=orig_peaks,
values=filtered_proba)
orig_peaks = np.unique(orig_peaks)
else:
orig_peaks = filtered_peaks
if verbose:
print("Everything done")
return orig_peaks, filtered_proba
def _filter_predictions(self, signal, preds):
"""
Filter model predictions.
Function filters model predictions by using following steps:
1. selects only the predictions that are above the given
probability threshold.
2. Correct these predictions upwards with respect the given ECG
3. Check if at least five points are corrected into the same
location.
4. If step 3 is true, then location is classified as an R-peak
5. Calculate probability of location being an R-peak by taking
mean of the probabilities from predictions in the same location.
Aforementioned steps can be thought as an noise reducing measure as
in original training data every R-peak was labeled with 5 points.
Parameters
----------
signal : array
Same signal that was used with extract_windows function. It is
used in correct_peaks function.
preds : array
Predictions for the sample points of the signal.
Returns
-------
filtered_peaks : array
locations of the filtered peaks.
filtered_probs : array
probability that filtered peak is an R-peak.
"""
assert (signal.shape == preds.shape)
# Select points probabilities and indices that are above
# self.threshold
above_thresh = preds[preds > self.threshold]
above_threshold_idx = np.where(preds > self.threshold)[0]
# Keep only points above self.threshold and correct them upwards
correct_up = processing.correct_peaks(sig=signal,
peak_inds=above_threshold_idx,
search_radius=5,
smooth_window_size=20,
peak_dir='up')
filtered_peaks = []
filtered_probs = []
for peak_id in np.unique(correct_up):
# Select indices and take probabilities from the locations
# that contain at leas 5 points
points_in_peak = np.where(correct_up == peak_id)[0]
if points_in_peak.shape[0] >= 5:
filtered_probs.append(above_thresh[points_in_peak].mean())
filtered_peaks.append(peak_id)
filtered_peaks = np.asarray(filtered_peaks)
filtered_probs = np.asarray(filtered_probs)
return filtered_peaks, filtered_probs
