"""
Signal Preprocessing Module for Physiological Signal Processing
This module provides comprehensive capabilities for physiological
signal processing including ECG, PPG, EEG, and other vital signs.
Author: vitalDSP Team
Date: 2025-01-27
Version: 1.0.0
Key Features:
- Multiple processing methods and functions
- NumPy integration for numerical computations
Examples:
--------
Basic usage:
>>> import numpy as np
>>> from vitalDSP.preprocess.noise_reduction import NoiseReduction
>>> signal = np.random.randn(1000)
>>> processor = NoiseReduction(signal)
>>> result = processor.process()
>>> print(f'Processing result: {result}')
"""
import numpy as np
from vitalDSP.transforms.wavelet_transform import WaveletTransform
from vitalDSP.filtering.signal_filtering import SignalFiltering
[docs]
def wavelet_denoising(signal, wavelet_name="haar", level=1):
"""
Performs wavelet denoising on the input signal using the custom wavelet transform.
Parameters
----------
signal : numpy.ndarray
The input signal to be denoised.
wavelet_name : str, optional (default='haar')
The type of wavelet to use.
level : int, optional (default=1)
The level of wavelet decomposition.
Returns
-------
denoised_signal : numpy.ndarray
The denoised signal.
Examples
--------
>>> signal = np.sin(np.linspace(0, 10, 100)) + np.random.normal(0, 0.1, 100)
>>> denoised_signal = wavelet_denoising(signal, wavelet_name='haar', level=2)
>>> print(denoised_signal)
"""
wavelet_transform = WaveletTransform(signal, wavelet_name=wavelet_name)
coeffs = wavelet_transform.perform_wavelet_transform(level=level)
# Apply soft thresholding to the detail coefficients
for i in range(len(coeffs) - 1):
sigma = np.median(np.abs(coeffs[i])) / 0.6745
uthresh = sigma * np.sqrt(2 * np.log(len(coeffs[i])))
coeffs[i] = np.sign(coeffs[i]) * np.maximum(np.abs(coeffs[i]) - uthresh, 0)
denoised_signal = wavelet_transform.perform_inverse_wavelet_transform(coeffs)
return denoised_signal
[docs]
def savgol_denoising(signal, window_length, polyorder):
"""
Applies Savitzky-Golay filtering for noise reduction.
Parameters
----------
signal : numpy.ndarray
The input signal to be denoised.
window_length : int
The length of the filter window (must be odd).
polyorder : int
The order of the polynomial to fit.
Returns
-------
denoised_signal : numpy.ndarray
The denoised signal.
Examples
--------
>>> signal = np.sin(np.linspace(0, 10, 100)) + np.random.normal(0, 0.1, 100)
>>> denoised_signal = savgol_denoising(signal, window_length=5, polyorder=2)
>>> print(denoised_signal)
"""
return SignalFiltering.savgol_filter(signal, window_length, polyorder)
[docs]
def gaussian_denoising(signal, sigma=1.0):
"""
Applies Gaussian filtering for noise reduction.
Parameters
----------
signal : numpy.ndarray
The input signal to be denoised.
sigma : float, optional (default=1.0)
The standard deviation of the Gaussian kernel.
Returns
-------
denoised_signal : numpy.ndarray
The denoised signal.
Examples
--------
>>> signal = np.sin(np.linspace(0, 10, 100)) + np.random.normal(0, 0.1, 100)
>>> denoised_signal = gaussian_denoising(signal, sigma=1.5)
>>> print(denoised_signal)
"""
signal_filter = SignalFiltering(signal)
return signal_filter.gaussian(sigma)
[docs]
def moving_average_denoising(signal, window_size):
"""
Applies moving average filtering for noise reduction.
Parameters
----------
signal : numpy.ndarray
The input signal to be denoised.
window_size : int
The size of the moving window.
Returns
-------
denoised_signal : numpy.ndarray
The denoised signal.
Examples
--------
>>> signal = np.sin(np.linspace(0, 10, 100)) + np.random.normal(0, 0.1, 100)
>>> denoised_signal = moving_average_denoising(signal, window_size=5)
>>> print(denoised_signal)
"""
signal_filter = SignalFiltering(signal)
return signal_filter.moving_average(window_size)