def estimate_hr_and_peaks(sampling_frequency, signal):
peaks = qrs_detector(sampling_frequency, signal, filter_length='3.5s')
# noinspection PyTypeChecker
instantaneous_rates = (sampling_frequency * 60) / np.diff(peaks)
# remove instantaneous rates which are lower than 30, higher than 240
selector = (instantaneous_rates > 30) & (instantaneous_rates < 240)
return {
'hr': float(np.nan_to_num(instantaneous_rates[selector].mean())),
'peaks': peaks
}
def estimate_average_heartrate(self, signal, sampling_frequency):
"""
Calculates the total average heart rate of the signal.
:param peaks: The detected peaks in the signal.
:param sampling_frequency: The sampling frequency of the signal.
:return: The calculated average heart rate.
"""
peaks = qrs_detector(sampling_frequency, signal)
instantaneous_rates = (sampling_frequency * 60) / np.diff(peaks)
# remove instantaneous rates which are lower than 30, higher than 240
selector = (instantaneous_rates > 30) & (instantaneous_rates < 240)
return float(np.nan_to_num(
instantaneous_rates[selector].mean())), peaks
def estimate_average_heartrate(s, sampling_frequency):
'''Estimates the average heart rate taking as base the input signal and its
sampling frequency.
This method will use the Pam-Tompkins detector available the MNE package to
clean-up and estimate the heart-beat frequency based on the ECG sensor
information provided.
Returns:
float: The estimated average heart-rate in beats-per-minute
'''
peaks = qrs_detector(sampling_frequency, s)
instantaneous_rates = (sampling_frequency * 60) / numpy.diff(peaks)
# remove instantaneous rates which are lower than 30, higher than 240
selector = (instantaneous_rates > 30) & (instantaneous_rates < 240)
return float(numpy.nan_to_num(instantaneous_rates[selector].mean())), peaks
def estimate_heartbeat_peaks(s, sampling_frequency):
'''Estimates the average heart rate taking as base the input signal and its
sampling frequency.
This method will use the Pam-Tompkins detector available the MNE package to
clean-up and estimate the heart-beat frequency based on the ECG sensor
information provided.
Returns:
float: The estimated average heart-rate in beats-per-minute
'''
peaks = qrs_detector(sampling_frequency, s)
try:
print('Length before handling outlier hr values: {0} minutes'.format(
peaks[-1] / (sampling_frequency * 60)))
# identify index of outlier peaks which lead to heart rate lower than 40, higher than 120
instantaneous_rates = (sampling_frequency * 60) / numpy.diff(peaks)
hr_selector = (instantaneous_rates >= 40) & (instantaneous_rates <=
120)
peaks_selector = numpy.insert(hr_selector, 0, True)
# print(selector_peaks, peaks)
# replace the outlier hr with the successive hr value
instantaneous_rates = instantaneous_rates[hr_selector]
time_stamps = peaks[
peaks_selector] / sampling_frequency # remove the timestamp of outlier peaks
time_stamps = time_stamps[
1:] # replace the initial hr with the first hr
assert len(instantaneous_rates) == len(time_stamps)
print('Length after handling outlier hr values: {0} minutes'.format(
time_stamps[-1] / 60))
return float(numpy.nan_to_num(instantaneous_rates.mean())
), peaks, instantaneous_rates, time_stamps
except:
print('No detected qrs peaks')
return -1.0, numpy.array([]), numpy.array([]), numpy.array([])
def run_algo(algorithm: str, sig: numpy.ndarray,
freq_sampling: int) -> List[int]:
"""
run a qrs detector on a signal
:param algorithm: name of the qrs detector to use
:type algorithm: str
:param sig: values of the sampled signal to study
:type sig: ndarray
:param freq_sampling: value of sampling frequency of the signal
:type freq_sampling: int
:return: localisations of qrs detections
:rtype: list(int)
"""
detectors = Detectors(freq_sampling)
if algorithm == 'Pan-Tompkins-ecg-detector':
qrs_detections = detectors.pan_tompkins_detector(sig)
elif algorithm == 'Hamilton-ecg-detector':
qrs_detections = detectors.hamilton_detector(sig)
elif algorithm == 'Christov-ecg-detector':
qrs_detections = detectors.christov_detector(sig)
elif algorithm == 'Engelse-Zeelenberg-ecg-detector':
qrs_detections = detectors.engzee_detector(sig)
elif algorithm == 'SWT-ecg-detector':
qrs_detections = detectors.swt_detector(sig)
elif algorithm == 'Matched-filter-ecg-detector' and freq_sampling == 360:
qrs_detections = detectors.matched_filter_detector(
sig, 'templates/template_360hz.csv')
elif algorithm == 'Matched-filter-ecg-detector' and freq_sampling == 250:
qrs_detections = detectors.matched_filter_detector(
sig, 'templates/template_250hz.csv')
elif algorithm == 'Two-average-ecg-detector':
qrs_detections = detectors.two_average_detector(sig)
elif algorithm == 'Hamilton-biosppy':
qrs_detections = bsp_ecg.ecg(signal=sig,
sampling_rate=freq_sampling,
show=False)[2]
elif algorithm == 'Christov-biosppy':
order = int(0.3 * freq_sampling)
filtered, _, _ = bsp_tools.filter_signal(signal=sig,
ftype='FIR',
band='bandpass',
order=order,
frequency=[3, 45],
sampling_rate=freq_sampling)
rpeaks, = bsp_ecg.christov_segmenter(signal=filtered,
sampling_rate=freq_sampling)
rpeaks, = bsp_ecg.correct_rpeaks(signal=filtered,
rpeaks=rpeaks,
sampling_rate=freq_sampling,
tol=0.05)
_, qrs_detections = bsp_ecg.extract_heartbeats(
signal=filtered,
rpeaks=rpeaks,
sampling_rate=freq_sampling,
before=0.2,
after=0.4)
elif algorithm == 'Engelse-Zeelenberg-biosppy':
order = int(0.3 * freq_sampling)
filtered, _, _ = bsp_tools.filter_signal(signal=sig,
ftype='FIR',
band='bandpass',
order=order,
frequency=[3, 45],
sampling_rate=freq_sampling)
rpeaks, = bsp_ecg.engzee_segmenter(signal=filtered,
sampling_rate=freq_sampling)
rpeaks, = bsp_ecg.correct_rpeaks(signal=filtered,
rpeaks=rpeaks,
sampling_rate=freq_sampling,
tol=0.05)
_, qrs_detections = bsp_ecg.extract_heartbeats(
signal=filtered,
rpeaks=rpeaks,
sampling_rate=freq_sampling,
before=0.2,
after=0.4)
elif algorithm == 'Gamboa-biosppy':
order = int(0.3 * freq_sampling)
filtered, _, _ = bsp_tools.filter_signal(signal=sig,
ftype='FIR',
band='bandpass',
order=order,
frequency=[3, 45],
sampling_rate=freq_sampling)
rpeaks, = bsp_ecg.gamboa_segmenter(signal=filtered,
sampling_rate=freq_sampling)
rpeaks, = bsp_ecg.correct_rpeaks(signal=filtered,
rpeaks=rpeaks,
sampling_rate=freq_sampling,
tol=0.05)
_, qrs_detections = bsp_ecg.extract_heartbeats(
signal=filtered,
rpeaks=rpeaks,
sampling_rate=freq_sampling,
before=0.2,
after=0.4)
elif algorithm == 'mne-ecg':
qrs_detections = mne_ecg.qrs_detector(freq_sampling, sig)
elif algorithm == 'heartpy':
rol_mean = rolling_mean(sig, windowsize=0.75, sample_rate=100.0)
qrs_detections = hp_pkdetection.detect_peaks(
sig, rol_mean, ma_perc=20, sample_rate=100.0)['peaklist']
elif algorithm == 'gqrs-wfdb':
qrs_detections = processing.qrs.gqrs_detect(sig=sig, fs=freq_sampling)
elif algorithm == 'xqrs-wfdb':
qrs_detections = processing.xqrs_detect(sig=sig, fs=freq_sampling)
else:
raise ValueError(
f'Sorry... unknown algorithm. Please check the list {algorithms_list}'
)
cast_qrs_detections = [int(element) for element in qrs_detections]
return cast_qrs_detections