def compute_moving_window_int(sample: np.ndarray,
fs: float,
blackman_win_length: int,
filter_length: int = 257,
delta: float = .02) -> np.ndarray:
"""
:param sample: ecg sample array
:param fs: sampling frequency
:param blackman_win_length: length of the blackman window on which to compute the moving window integration
:param filter_length: length of the FIR bandpass filter on which filtering is done on ecg sample array
:param delta: to compute the weights of each band in FIR filter
:return: the Moving window integration of the sample array
"""
# I believe these constants can be kept in a file
# filter edges
filter_edges = [0, 4.5 * 2 / fs, 5 * 2 / fs, 20 * 2 / fs, 20.5 * 2 / fs, 1]
# gains at filter band edges
gains = [0, 0, 1, 1, 0, 0]
# weights
weights = [500 / delta, 1 / delta, 500 / delta]
# length of the FIR filter
# FIR filter coefficients for bandpass filtering
filter_coeff = signal.firls(filter_length, filter_edges, gains, weights)
# bandpass filtered signal
bandpass_signal = signal.convolve(sample, filter_coeff, 'same')
bandpass_signal /= np.percentile(bandpass_signal, 90)
# derivative array
derivative_array = (np.array([-1.0, -2.0, 0, 2.0, 1.0])) * (1 / 8)
# derivative signal (differentiation of the bandpass)
derivative_signal = signal.convolve(bandpass_signal, derivative_array, 'same')
derivative_signal /= np.percentile(derivative_signal, 90)
# squared derivative signal
derivative_squared_signal = derivative_signal ** 2
derivative_squared_signal /= np.percentile(derivative_squared_signal, 90)
# blackman window
blackman_window = np.blackman(blackman_win_length)
# moving window Integration of squared derivative signal
mov_win_int_signal = signal.convolve(derivative_squared_signal, blackman_window, 'same')
mov_win_int_signal /= np.percentile(mov_win_int_signal, 90)
return mov_win_int_signal
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