bcgunet/bcgunet.py

from os.path import *
import numpy as np
import random
import torch
import torch.nn as nn
import time
import tqdm
from scipy.signal import butter, sosfilt
from .unet import UNet1d


def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    sos = butter(order, [low, high], analog=False, btype="band", output="sos")
    y = sosfilt(sos, data)
    return y


def norm(ecg):
    min1, max1 = np.percentile(ecg, [1, 99])
    ecg[ecg > max1] = max1
    ecg[ecg < min1] = min1
    ecg = (ecg - min1) / (max1 - min1)
    return ecg


def run(
    input_eeg,
    input_ecg=None,
    sfreq=5000,
    iter_num=5000,
    winsize_sec=2,
    lr=1e-3,
    onecycle=True,
):
    window = winsize_sec * sfreq
    eeg_raw = input_eeg
    eeg_channel = eeg_raw.shape[0]

    eeg_filtered = eeg_raw * 0
    t = time.time()
    for ii in range(eeg_channel):
        eeg_filtered[ii, ...] = butter_bandpass_filter(
            eeg_raw[ii, :], 0.5, sfreq * 0.4, sfreq
        )

    baseline = eeg_raw - eeg_filtered

    if input_ecg is None:
        from sklearn.decomposition import PCA

        pca = PCA(n_components=1)
        ecg = norm(pca.fit_transform(eeg_filtered.T)[:, 0].flatten())
    else:
        ecg = norm(input_ecg.flatten())

    torch.cuda.empty_cache()
    device = "cuda" if torch.cuda.is_available() else "cpu"
    NET = UNet1d(n_channels=1, n_classes=eeg_channel, nfilter=8).to(device)
    optimizer = torch.optim.Adam(NET.parameters(), lr=lr)
    optimizer.zero_grad()
    maxlen = ecg.size
    if onecycle:
        scheduler = torch.optim.lr_scheduler.OneCycleLR(
            optimizer, lr, total_steps=iter_num
        )
    else:
        # constant learning rate
        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=1)

    loss_list = []

    # randomly get  windows in ECG signal

    index_all = (np.random.random_sample(iter_num) * (maxlen - window)).astype(int)

    pbar = tqdm.tqdm(index_all)
    count = 0
    for index in pbar:
        count += 1
        ECG = ecg[index : (index + window)]
        EEG = eeg_filtered[:, index : (index + window)]
        ECG_d = torch.from_numpy(ECG[None, ...][None, ...]).to(device).float()
        EEG_d = torch.from_numpy(EEG[None, ...]).to(device).float()

        # step 3: forward path of UNET
        logits = NET(ECG_d)
        loss = nn.MSELoss()(logits, EEG_d)
        loss_list.append(loss.item())

        # Step 5: Perform back-propagation
        loss.backward()  # accumulate the gradients
        optimizer.step()  # Update network weights according to the optimizer
        optimizer.zero_grad()  # empty the gradients
        scheduler.step()

        if count % 50 == 0:
            pbar.set_description(
                f"Loss {np.mean(loss_list):.3f}, lr: {optimizer.param_groups[0]['lr']:.5f}"
            )
            loss_list = []

    EEG = eeg_filtered
    # ECG = norm(butter_bandpass_filter(data['ECG'], 0.5, 20, sfreq))
    ECG = ecg
    ECG_d = torch.from_numpy(ECG[None, ...][None, ...]).to(device).float()
    EEG_d = torch.from_numpy(EEG[None, ...]).to(device).float()
    with torch.no_grad():
        logits = NET(ECG_d)
    BCG_pred = logits.cpu().detach().numpy()[0, ...]

    neweeg = EEG - BCG_pred + baseline

    return neweeg


def morlet_psd(signal, sample_rate=5000, freq=10, wavelet="morl"):
    import pywt

    # Define the wavelet and scales to be used

    scales = np.arange(sample_rate)
    freqs = pywt.scale2frequency("morl", scales) * sample_rate
    indx = np.argmin(abs(freqs - freq))

    scale = scales[indx]

    # scale = pywt.frequency2scale('morl', freq/sample_rate)

    # Calculate the wavelet coefficients
    coeffs, freq = pywt.cwt(signal, scale, wavelet, 1 / sample_rate)
    # Calculate the power (magnitude squared) of the coefficients
    power = np.abs(coeffs) ** 2

    # Average the power across time to get the power spectral density
    psd = np.mean(power, axis=1)

    return psd


def get_psd(eeg, sfreq=5000, freq=10):
    psd = []
    for ii in tqdm.tqdm(range(eeg.shape[0])):
        psd.append(morlet_psd(eeg[ii], sample_rate=sfreq, freq=freq))

    return np.array(psd)