def filterResp(self, x):
# fc = 2. # Cutoff frequency as a fraction of the sampling rate (in (0, 0.5)).
# b = 0.08 # 120 taps
# N = int(np.ceil((4 / b)))
# if not N % 2: N += 1 # Make sure that N is odd.
# n = np.arange(N)
#
# # Compute sinc filter.
# h = np.sinc(2 * fc * (n - (N - 1) / 2))
#
# # Compute Blackman window.
# w = 0.42 - 0.5 * np.cos(2 * np.pi * n / (N - 1)) + \
# 0.08 * np.cos(4 * np.pi * n / (N - 1))
#
# # Multiply sinc filter by window.
# h = h * w
#
# # Normalize to get unity gain.
# h = h / np.sum(h)
#
# return np.convolve(x, h, mode="same")
filtered, _, _ = st.filter_signal(signal=x,
ftype='butter',
band='bandpass',
order=2,
frequency=[0.1, 0.35],
sampling_rate=self.fs)
return filtered
def one_lead_ecg_filter(signal, sampling_rate=500):
filtered, _, _ = st.filter_signal(signal=signal,
ftype='FIR',
band='bandpass',
order=int(0.3 * sampling_rate),
frequency=[3, 45],
sampling_rate=sampling_rate)
return filtered
def filterECG(self, x):
order = int(0.3 * self.fs)
filtered, _, _ = st.filter_signal(signal=x,
ftype='FIR',
band='bandpass',
order=order,
frequency=[3, 45],
sampling_rate=self.fs)
return filtered
def filterPPG(self, x):
# filter signal
# filtered = butterBandpassFilter(x, lowcut=.5, highcut=5., order=4, fs=self.fs)
filtered, _, _ = st.filter_signal(signal=x,
ftype='butter',
band='bandpass',
order=4,
frequency=[1, 7],
sampling_rate=self.fs)
return filtered
def filterEDA(self, x):
filtered, _, _ = st.filter_signal(signal=x,
ftype='butter',
band='lowpass',
order=2,
frequency=15,
sampling_rate=self.fs)
sm_size = int(0.75 * self.fs)
filtered, _ = st.smoother(signal=filtered,
kernel='boxzen',
size=sm_size,
mirror=True)
return filtered
def _positive_peaks(raw_ECG_data, sampling_rate=1000, method='wavelet'):
"""Process a raw ECG signal and extracts R peaks.
Parameters
----------
signal : array
Raw ECG signal.
sampling_rate : int, float, optional
Sampling frequency (Hz).
method: 'wavelet' or 'hamilton'. Hamilton will find R-peaks based on the
iterative hamilton segmenter method. Wavelet find find r-peaks based
on convolution of the signal with a qrs-complex-shaped wavelet.
Returns
-------
positive_peaks : array
Positive-peak location indices.
"""
if method == 'hamilton':
order = int(0.3 * sampling_rate)
filtered, _, _ = st.filter_signal(raw_ECG_data,
ftype='FIR',
band='bandpass',
order=order,
frequency=[3, 45],
sampling_rate=sampling_rate)
positive_peaks, = biosppy.signals.ecg.hamilton_segmenter(
filtered, sampling_rate=1000.0)
positive_peaks, = biosppy.signals.ecg.correct_rpeaks(
signal=filtered,
rpeaks=positive_peaks,
sampling_rate=1000,
tol=0.05)
# plt.plot(raw_ECG_data)
# plt.plot(positive_peaks, raw_ECG_data[positive_peaks], 'or')
elif method == 'wavelet':
wv = pywt.Wavelet('sym4')
_, qrs, _ = wv.wavefun(level=5)
cv = signal.fftconvolve(raw_ECG_data, qrs, mode='same')
positive_peaks = signal.find_peaks(np.abs(cv),
distance=int(sampling_rate / 2),
prominence=200)[0]
# plt.figure()
# plt.plot(cv)
# plt.plot(raw_ECG_data)
# plt.plot(positive_peaks, cv[positive_peaks], 'or')
else:
raise ValueError('no valid method selected')
return positive_peaks