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dfilters.py
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dfilters.py
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# -*- coding: utf-8 -*-
# ============================================================
#
# BWL FIR Filtering
# authors: David Castro Piñol, Francisco Perdigon Romero
# email: davidpinyol91@gmail.com, fperdigon88@gmail.com
# github id: Dacapi91, fperdigon
#
# ============================================================
import scipy.io as sio
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import kaiserord, firwin, filtfilt, butter
def FIRRemoveBL(ecgy, Fs, Fc, factor):
# ecgy: the contamined signal (must be a list)
# Fc: cut-off frequency
# Fs: sample frequiency
# ECG_Clean : processed signal without BLW
# getting the length of the signal
signal_len = len(ecgy)
# The Nyquist rate of the signal.
nyq_rate = Fs / 2.0
# The desired width of the transition from stop to pass,
# relative to the Nyquist rate.
width = 0.07/nyq_rate
# Attenuation in the stop band, in dB.
# related to devs in Matlab. On Matlab is on proportion
ripple_db = round(-20*np.log10(0.001))+1
ripple_db = ripple_db / factor
# Compute the order and Kaiser parameter for the FIR filter.
N, beta = kaiserord(ripple_db, width)
# Use firwin with a Kaiser window to create a highpass FIR filter.
h = firwin(N, Fc/nyq_rate, window=('kaiser', beta), pass_zero='highpass')
# Check filtfilt condition
if N*3 > signal_len:
diff = N*3 - signal_len
ecgy = list(reversed(ecgy)) + list(ecgy) + list(ecgy[-1] * np.ones(diff))
# Filtering with filtfilt
ECG_Clean = filtfilt(h, 1.0, ecgy)
ECG_Clean = ECG_Clean[signal_len: signal_len + signal_len]
else:
ECG_Clean = filtfilt(h, 1.0, ecgy)
return ECG_Clean, N
def FIRRemoveHF(ecgy, Fs, Fc, factor):
# ecgy: the contamined signal (must be a list)
# Fc: cut-off frequency
# Fs: sample frequiency
# ECG_Clean : processed signal without BLW
# getting the length of the signal
signal_len = len(ecgy)
# The Nyquist rate of the signal.
nyq_rate = Fs / 2.0
# The desired width of the transition from stop to pass,
# relative to the Nyquist rate.
width = 0.07 / nyq_rate
# Attenuation in the stop band, in dB.
# related to devs in Matlab. On Matlab is on proportion
ripple_db = round(-20 * np.log10(0.001)) + 1
ripple_db = ripple_db / factor
# Compute the order and Kaiser parameter for the FIR filter.
N, beta = kaiserord(ripple_db, width)
# Use firwin with a Kaiser window to create a highpass FIR filter.
h = firwin(N, Fc / nyq_rate, window=('kaiser', beta), pass_zero='lowpass')
# Check filtfilt condition
if N * 3 > signal_len:
diff = N * 3 - signal_len
ecgy = list(reversed(ecgy)) + list(ecgy) + list(ecgy[-1] * np.ones(diff))
# Filtering with filtfilt
ECG_Clean = filtfilt(h, 1.0, ecgy)
ECG_Clean = ECG_Clean[signal_len: signal_len + signal_len]
else:
ECG_Clean = filtfilt(h, 1.0, ecgy)
return ECG_Clean, N
def IIRRemoveBL(ecgy,Fs, Fc):
# ecgy: the contamined signal (must be a list)
# Fc: cut-off frequency
# Fs: sample frequiency
# ECG_Clean : processed signal without BLW
# getting the length of the signal
signal_len = len(ecgy)
# fixed order
N = 4
# Normalized Cutt of frequency
Wn = Fc/(Fs/2)
# IIR butterworth coefficients
b, a = butter(N, Wn, 'highpass', analog=False)
# Check filtfilt condition
if N*3 > signal_len:
diff = N*3 - signal_len
ecgy = list(reversed(ecgy)) + list(ecgy) + list(ecgy[-1] * np.ones(diff))
# Filtering with filtfilt
ECG_Clean = filtfilt(b, a, ecgy)
ECG_Clean = ECG_Clean[signal_len: signal_len + signal_len]
else:
ECG_Clean = filtfilt(b, a, ecgy)
return ECG_Clean
def IIRRemoveHF(ecgy, Fs, Fc):
# ecgy: the contamined signal (must be a list)
# Fc: cut-off frequency
# Fs: sample frequiency
# ECG_Clean : processed signal without BLW
# getting the length of the signal
signal_len = len(ecgy)
# fixed order
N = 4
# Normalized Cutt of frequency
Wn = Fc / (Fs / 2)
# IIR butterworth coefficients
b, a = butter(N, Wn, 'lowpass', analog=False)
# Check filtfilt condition
if N * 3 > signal_len:
diff = N * 3 - signal_len
ecgy = list(reversed(ecgy)) + list(ecgy) + list(ecgy[-1] * np.ones(diff))
# Filtering with filtfilt
ECG_Clean = filtfilt(b, a, ecgy)
ECG_Clean = ECG_Clean[signal_len: signal_len + signal_len]
else:
ECG_Clean = filtfilt(b, a, ecgy)
return ECG_Clean
def FIR_test_Dataset(Dataset):
[train_set, train_set_GT, X_test, y_test] = Dataset
## parameters
Fs = 360
Fc_l = 0.67
Fc_h = 150.0
y_filter_out = []
current_signal = 0
for signal in X_test:
current_signal += 1
print('(FIR) Filtering signal ' + str(current_signal) + ' of ' + str(len(X_test)))
s = np.squeeze(signal, axis=1).tolist()
temp_signal, N = FIRRemoveBL(s, Fs, Fc_l, 4.5)
temp_signal, N = FIRRemoveHF(temp_signal, Fs, Fc_h, 4.5)
y_filter_out.append(temp_signal)
y_filter_out = np.expand_dims(np.array(y_filter_out), axis=2)
return [X_test, y_test, y_filter_out]
def IIR_test_Dataset(Dataset):
[train_set, train_set_GT, X_test, y_test] = Dataset
## parameters
Fs = 360
Fc_l = 0.67
Fc_h = 150.0
y_filter_out = []
current_signal = 0
for signal in X_test:
current_signal += 1
print('(IIR) Filtering signal ' + str(current_signal) + ' of ' + str(len(X_test)))
s = np.squeeze(signal, axis=1).tolist()
temp_signal = IIRRemoveBL(s, Fs, Fc_l)
temp_signal = IIRRemoveHF(temp_signal, Fs, Fc_h)
y_filter_out.append(temp_signal)
y_filter_out = np.expand_dims(np.array(y_filter_out), axis=2)
return [X_test, y_test, y_filter_out]
if __name__ == "__main__":
# signal for demonstration.
ecgy = sio.loadmat('ecgbeat.mat')
signal = ecgy['ecgy']
signal = list(signal[:,0])
## parameters
Fs = 360
Fc = 0.67
factor = 2
#ECG_Clean,N = FIRRemoveBL(signal,Fs,Fc,factor)
ECG_Clean = IIRRemoveBL(signal,Fs, Fc)
plt.figure()
plt.plot(signal[0:len(ecgy['ecgy'])])
plt.plot(ECG_Clean)
plt.show()
plt.figure()