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# +
import base64
import struct
import numpy as np
import xmltodict
from scipy.ndimage import median_filter as scipy_ndimage_median_filter
import matplotlib.pyplot as plt
import pywt
# -
lead_order = [
"I",
"II",
"III",
"aVR",
"aVL",
"aVF",
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
]
def plot_12_lead_ecg(ecg_array, output_filename=None):
"""
Plot each lead of the 12-lead ECG, and save the plot to a file.
All leads share the x axis, but each lead gets its own chart.
"""
fig, axs = plt.subplots(12, 1, sharex=True, figsize=(16, 9))
for lead, lead_name in enumerate(lead_order):
axs[lead].plot(ecg_array[:, lead])
axs[lead].set_ylabel(str(lead_name))
if output_filename is not None:
plt.savefig(output_filename)
plt.show()
plt.close()
def get_median_filter_width(sampling_frequency, duration):
res = int(sampling_frequency * duration)
res += (res % 2) - 1 # needs to be an odd number
return res
def remove_baseline_wander(waveform: np.ndarray, sampling_frequency: int) -> np.ndarray:
"""
Remove baseline wander from ECG NPYs
de Chazal et al. used two median filters to remove baseline wander.
Median filters take the median value of a sliding window of a specified size
One median filter of 200-ms width to remove QRS complexes and P-waves and other of
600 ms width to remove T-waves.
Do one filter and then the next filter. Then take the result and subtract it form the original signal
https://pubmed.ncbi.nlm.nih.gov/15248536/
Example of median filter:
medfilt([2,6,5,4,0,3,5,7,9,2,0,1], 5) -> [ 2. 4. 4. 4. 4. 4. 5. 5. 5. 2. 1. 0.]
>>> np.median([0, 0, 2, 6, 5])
2.0
>>> np.median([0, 2, 6, 5, 4])
4.0
"""
# Depending on the sampling frequency, the widths of the convolutional median filters changes
filter_widths = [
get_median_filter_width(sampling_frequency, duration) for duration in [0.2, 0.6]
]
filter_widths = np.array(filter_widths, dtype="int")
# make a copy of orignal signal
original_waveform = waveform.copy()
# apply median filters one by one on top of each other
for filter_width in filter_widths:
waveform = scipy_ndimage_median_filter(
waveform, size=(filter_width, 1), mode="constant", cval=0.0
)
waveform = original_waveform - waveform # finally subtract from orignal signal
return waveform
def wavelet_denoise_signal(
waveform: np.ndarray,
dwt_transform: str = "bior4.4",
dlevels: int = 9,
cutoff_low: int = 1,
cutoff_high: int = 7,
) -> np.ndarray:
# cutoff_low determines how flat you want overall baseline to be.
# Higher means more flat baseline
# cutoff_high determines within the small segments how much do
# you want to suppress the squiggliness. Lower cutoff_high
# suppresses more squiggliness but also suppresses R wave morphology
coefficients = pywt.wavedec(
waveform, dwt_transform, level=dlevels
) # wavelet transform 'bior4.4'
# scale 0 to cutoff_low
for low_cutoff_value in range(0, cutoff_low):
coefficients[low_cutoff_value] = np.multiply(
coefficients[low_cutoff_value], [0.0]
)
# scale cutoff_high to end
for high_cutoff_value in range(cutoff_high, len(coefficients)):
coefficients[high_cutoff_value] = np.multiply(
coefficients[high_cutoff_value], [0.0]
)
waveform = pywt.waverec(coefficients, dwt_transform) # inverse wavelet transform
return waveform
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