Parse CARTO point signals

Full example code: examples/parser/carto_point_signal.py

Each datapoint has a corresponding ECG export file, for example 1-AT1260_P66_ECG_Export.txt. We can parse the point signals (Unipolar, bipolar, CS and ECG) from this file.

from dgmr.parser import CartoReader

pointfile = "path_to_your_point_signal_file"
signal_data = CartoReader.load_signal(pointfile)

print(signal_data[:5])

Output:

   Unipolar  Bipolar     CS    ECG
0     2.067   -0.075  0.003 -0.027
1     2.073   -0.087 -0.003 -0.024
2     2.079   -0.102  0.000 -0.024
3     2.082   -0.117  0.003 -0.021
4     2.082   -0.126  0.003 -0.021

We can also load the signals of multiple points by supplying the root file name (map name) and point idx. For example, if our map is called 1-AT1260 and we want to load signals of points with id 66, 67 and 68, the corresponding files will be

• 1-AT1260_P66_ECG_Export.txt
• 1-AT1260_P66_ECG_Export.txt
• 1-AT1260_P66_ECG_Export.txt

root = "your_folder" # Folder where the export files are located
filetag = "your_map_name" # Root file name
point_idx = [66, 67, 68]

signals_data = CartoReader.load_signals(root, filetag, point_idx)

Visualization

Using matplotlib, we can easily visualize the signals of a single point using the parsed data.

import numpy as np
import matplotlib.pyplot as plt

# Different channels of a single point
channels = ["Unipolar", "Bipolar", "CS", "ECG"]

fig, axs = plt.subplots(nrows=4)
for i, col in enumerate(channels):
    arr = np.array(signal_data[col])
    axs[i].plot(arr, label=col)
    axs[i].legend(loc="upper right")
plt.tight_layout()
plt.show()

Or the same signal of multiple points. In this case, the ECG signal.

import numpy as np
import matplotlib.pyplot as plt

# ECG channel of different points
ecg_data = signals_data["ECG"]
ecg_array = np.array(ecg_data.tolist())
timesteps = np.arange(ecg_array.shape[1])

fig, ax = plt.subplots(figsize=(12,5))
for arr in ecg_array:
    ax.plot(timesteps, arr)
plt.show()