| import subprocess |
| import pandas as pd |
| import numpy as np |
| import ast |
| import os |
| import tqdm |
| import shutil |
| import wfdb |
| from scipy import signal |
|
|
| ''' |
| Function to create patches for NeuroRVQ |
| ''' |
| def create_patches(ppg_signal, maximum_patches, patch_size, channels_use): |
| n, c, t = ppg_signal.shape |
| n_time = (maximum_patches // len(channels_use)) |
| ppg_signal = ppg_signal[:, :, :n_time * patch_size] |
| ppg_signal_patches = ppg_signal[:, channels_use, :] |
| return ppg_signal_patches, n_time |
|
|
|
|
| |
| record = wfdb.rdrecord( |
| "bidmc03", |
| pn_dir="bidmc/1.0.0" |
|
|
| ) |
| |
| ppg_signal = record.p_signal[:, record.sig_name.index('PLETH,')] |
| ppg_signal = ppg_signal.reshape(1, -1) |
|
|
| |
| highpass = 0.5 |
| lowpass = 40 |
| lowpass_applied = min(lowpass, record.fs / 2) - 0.5 |
| [b, a] = signal.butter(N=3, Wn=[highpass, lowpass_applied], btype='bandpass', fs=record.fs) |
| ppg_signal = signal.filtfilt(b, a, ppg_signal, axis=-1) |
| |
| ppg_signal = signal.resample(ppg_signal, num=int(ppg_signal.shape[1] / record.fs * 100), axis=-1) |
| ppg_signal = ppg_signal.astype('float16') |
| ppg_signal = ppg_signal.reshape(1, 1, ppg_signal.shape[-1]) |
| np.save("./example_files/ppg_sample/example_ppg.npy", ppg_signal) |
|
|
