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import numpy as np
from scipy.signal import resample, butter, filtfilt
from baseline_wander_removal import bw_remover
import pywt
import matplotlib.pyplot as plt
def normalize(sig, val=2):
return val*((sig-np.min(sig))/(np.max(sig)-np.min(sig)))
def butter_lowpass_filter(data, cutoff, fs, order):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
# Get the filter coefficients
b, a = butter(order, normal_cutoff, btype='low', analog=False)
y = filtfilt(b, a, data)
return y
def visualize_sig(sig, filename="test.png"):
fig, ax = plt.subplots(3, 1, figsize=(10, 10))
ax[0].plot(sig[0])
ax[1].plot(sig[1])
ax[2].plot(sig[2])
# plt.show()
plt.savefig(filename)
def preprocess_one_chunk(chunk, lvl=2):
chunk = resample(chunk, 1000)
chunk = pywt.wavedec(chunk, 'db6', level=lvl)[0]
# print(x.shape)
# x = pad(x)
chunk = normalize(chunk)
return chunk
def prepare_all_leads(path, butter_filter=False):
if path.endswith(".txt"):
signal = np.loadtxt(path, delimiter=',', unpack=True)
elif path.endswith(".npy"):
sig = np.load(path, allow_pickle=True)
x = pywt.wavedec(sig[0], 'db6', level=2)[0]
y = pywt.wavedec(sig[1], 'db6', level=2)[0]
z = pywt.wavedec(sig[2], 'db6', level=2)[0]
return x[None, :], y[None, :], z[None, :]
freq = signal.shape[1] // 60
print(freq)
if freq < 250:
lvl = 1
else:
lvl = 2
sig = [signal[0], signal[1], signal[2]]
sig[0] = bw_remover(freq, sig[0])
sig[1] = bw_remover(freq, sig[1])
sig[2] = bw_remover(freq, sig[2])
if butter_filter:
cutoff = 20 # desired cutoff frequency of the filter, Hz , slightly higher than actual 1.2 Hz
order = 1 # sin wave can be approx represented as quadratic
sig[0] = butter_lowpass_filter(sig[0], cutoff, freq, order)
sig[1] = butter_lowpass_filter(sig[1], cutoff, freq, order)
sig[2] = butter_lowpass_filter(sig[2], cutoff, freq, order)
sig_length = freq*2
total_samples = sig[0].shape[0] // 1000
lead_1 = []
lead_2 = []
lead_3 = []
for i in range(total_samples):
x = sig[0][i*sig_length:(i+1)*sig_length]
y = sig[1][i*sig_length:(i+1)*sig_length]
z = sig[2][i*sig_length:(i+1)*sig_length]
x = preprocess_one_chunk(x, lvl=lvl)
y = preprocess_one_chunk(y, lvl=lvl)
z = preprocess_one_chunk(z, lvl=lvl)
lead_1.append(x)
lead_2.append(y)
lead_3.append(z)
return np.asarray(lead_1), np.asarray(lead_2), np.asarray(lead_3)
def pad(sig):
sig = np.pad(sig, (0, 258-sig.shape[0]), 'constant')
return sig |