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import shutil
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import shutil
import librosa
from scipy.signal import iirnotch, lfilter
from data_utils import butter_bandpass_filter, correct_ecg_inversion
import pickle
import pandas as pd
BASE_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'datasets')
NOTCH_FREQ = 60
EEG_LOWCUT = 0.3
EEG_HIGHCUT = 40
EMG_LOWCUT = 10
EMG_HIGHCUT = 1000
ECG_LOWCUT = 0.3
ECG_HIGHCUT = 60
FLOW_LOWCUT = 0
FLOW_HIGHCUT = 15
NUM_PROCESS = 4
import argparse
import multiprocessing
TARGET_RESAMPLE = 100
def process_ecg(args):
sig, chn_grp, chn = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
sampling_rate = data.shape[1] // (30)
data = data.reshape(-1, 1)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, ECG_LOWCUT, ECG_HIGHCUT, sampling_rate)
b_notch, a_notch = iirnotch(NOTCH_FREQ, 30, sampling_rate)
filtered = lfilter(b_notch, a_notch, filtered)
filtered = filtered.reshape(-1, sampling_rate*30)
# filtered = librosa.resample(filtered, orig_sr=SAMPLING_RATE, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered_epochs = []
for epoch in filtered:
inv_epoch, _ = correct_ecg_inversion(epoch, TARGET_RESAMPLE)
filtered_epochs.append(inv_epoch)
filtered = np.stack(filtered_epochs, axis=0)
if sampling_rate != TARGET_RESAMPLE:
filtered = np.reshape(filtered, (-1,1))
filtered = librosa.resample(filtered, orig_sr=sampling_rate, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def process_eeg(args):
sig, chn_grp, chn = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
sampling_rate = data.shape[1] // (30)
data = data.reshape(-1, 1)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, EEG_LOWCUT, EEG_HIGHCUT, sampling_rate)
b_notch, a_notch = iirnotch(NOTCH_FREQ, 30, sampling_rate)
filtered = lfilter(b_notch, a_notch, filtered)
if sampling_rate != TARGET_RESAMPLE:
filtered = librosa.resample(filtered, orig_sr=sampling_rate, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def process_emg(args):
sig, chn_grp, chn = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
sampling_rate = data.shape[1] // (30)
data = data.reshape(-1, 1)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, EMG_LOWCUT, EMG_HIGHCUT, sampling_rate)
b_notch, a_notch = iirnotch(NOTCH_FREQ, 30, sampling_rate)
filtered = lfilter(b_notch, a_notch, filtered)
if sampling_rate != TARGET_RESAMPLE:
filtered = librosa.resample(filtered, orig_sr=sampling_rate, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def process_flow(args):
sig, chn_grp, chn = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
sampling_rate = data.shape[1] // (30)
data = data.reshape(-1, 1)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, FLOW_LOWCUT, FLOW_HIGHCUT, sampling_rate)
if sampling_rate != TARGET_RESAMPLE:
filtered = librosa.resample(filtered, orig_sr=sampling_rate, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
# median filter?
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def save_mmap(results, sig_list, chn_grp, chn, split='train'):
signame_list = [sig.split('.')[0] for sig in sig_list]
epoch_lengths = [sig.shape[0] for sig in results]
metadata = dict()
sig_info = dict()
start, end = 0, 0
for signame, sig in zip(signame_list, results):
length = sig.shape[0]
end = start + length
info = dict()
info['pos'] = (start, end)
info['stats'] = {'sum': float(np.sum(sig)), 'sumsq': float(np.sum(sig ** 2))}
sig_info[signame] = info
start = end
mmap_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.mmap')
signals_cat = np.concatenate(results, axis=0).astype(np.float32)
os.makedirs(os.path.dirname(mmap_path), exist_ok=True)
data = np.memmap(mmap_path, dtype=np.float32, mode='w+', shape=signals_cat.shape)
data[:] = signals_cat
data.flush()
metadata['sig_info'] = sig_info
metadata['data_shape'] = signals_cat.shape
info_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.pickle')
with open(info_path, 'wb') as f:
pickle.dump(metadata, f)
def preprocess(sig_list, chn_grp=None, chn=None, split='train'):
def _process(sig_list, chn_grp, chn, split):
print(f"Processing {chn_grp}/{chn}")
# if not (chn == "C4-A1" or chn == "AIRFLOW"): continue
if chn_grp in ['EEG','EOG']:
# sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
input_list = [(sig, chn_grp, chn) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_eeg, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
elif chn_grp in ['EMG']:
# sig_list = os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn))
input_list = [(sig, chn_grp, chn) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_emg, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
elif chn_grp in ['ECG']:
# sig_list = os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn))
input_list = [(sig, chn_grp, chn) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_ecg, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
elif chn_grp in ['ABD', 'CHEST', 'AIRFLOW']:
# sig_list = os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn))
input_list = [(sig, chn_grp, chn) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_flow, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
else:
results = []
for sig in tqdm(sig_list):
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
sampling_rate = data.shape[1] // (30)
if sampling_rate != TARGET_RESAMPLE or chn == 'SaO2':
# assuming 30s epochs
data = data.reshape(-1, 1)
if chn == 'SaO2':
data = librosa.resample(data, orig_sr=sampling_rate, target_sr=1, axis=0, res_type='soxr_qq')
data = data / 1e8 # data range 0 to 1e8
if np.max(data) > 1 or np.min(data) < 0:
print(f"Warning: SaO2 values out of range: {sig} with min {np.min(data)} and max {np.max(data)}")
data = data.reshape(-1, 1*30)
else:
data = librosa.resample(data, orig_sr=sampling_rate, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
data = data.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, data)
elif cfg.save_type == 'mmap':
results.append(data)
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
if chn_grp is not None and chn is not None:
_process(sig_list, chn_grp, chn, split)
return
else:
for chn_grp in os.listdir(os.path.join(BASE_PATH, cfg.data_dir)):
if not os.path.isdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp)):
continue
for chn in os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp)):
if not os.path.isdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)):
continue
_process(sig_list, chn_grp, chn, split)
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', default='shhs1/signals_MAE', help='SHHS raw data location')
parser.add_argument('--output_dir', default='shhs1/mmap_signals_MAE_processed', help='SHHS processed data location')
parser.add_argument('--ann_split', action='store_true', help='To split annotation files into train/val/test')
parser.add_argument('--save_type', default='mmap', help='Which data type to save. "npy" or "mmap"')
parser.add_argument('--chn_grp', default=None, help='If specified, only preprocess this channel group')
parser.add_argument('--chn', default=None, help='If specified, only preprocess this channel')
parser.add_argument('--shhs2', action='store_true', help='SHHS2 preprocessing?')
cfg = parser.parse_args()
if __name__ == '__main__':
if not cfg.shhs2:
def get_shhs_lists(shhs_split_file):
ids = pd.read_csv(os.path.join("shhs_meta_data", shhs_split_file), header=None, names=['a'])
ids = ids['a'].values.tolist()
list_files = ["shhs1-20"+id[11:15] for id in ids]
return [id + ".npy" for id in list_files]
list_files_train = get_shhs_lists("train_list.txt")
list_files_val = get_shhs_lists("eval_list.txt")
list_files_test = get_shhs_lists("test_list.txt")
sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, "EEG", "C3-A2")))
list_files_train = list(set(list_files_train).intersection(set(sig_list)))
list_files_val = list(set(list_files_val).intersection(set(sig_list)))
list_files_test = list(set(list_files_test).intersection(set(sig_list)))
print("Number of records in training set:", len(list_files_train))
print("Number of records in validation set:", len(list_files_val))
print("Number of records in test set:", len(list_files_test))
else:
# For SHHS2, use all data for preprocessing
sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, "EEG", "C3-A2")))
list_files_train = sig_list
list_files_val = []
list_files_test = []
print("Processing training set...")
preprocess(list_files_train, cfg.chn_grp, cfg.chn, split='train')
print("Processing evaluation set...")
preprocess(list_files_val, cfg.chn_grp, cfg.chn, split='val')
print("Processing test set...")
preprocess(list_files_test, cfg.chn_grp, cfg.chn, split='test')
# Reorganize annotations files
if cfg.ann_split and not cfg.shhs2:
ANN_PATH = os.path.join(os.path.dirname(os.path.join(BASE_PATH, cfg.data_dir)), 'annotations')
NEW_ANN_PATH = ANN_PATH.replace('annotations', 'annotations_split')
os.makedirs(NEW_ANN_PATH, exist_ok=True)
os.makedirs(os.path.join(NEW_ANN_PATH,'train'), exist_ok=True)
os.makedirs(os.path.join(NEW_ANN_PATH,'val'), exist_ok=True)
os.makedirs(os.path.join(NEW_ANN_PATH,'test'), exist_ok=True)
for fname in tqdm(list_files_train):
ann_fname = fname.replace('.npy', '_ann.npy')
shutil.copy(os.path.join(ANN_PATH, ann_fname), os.path.join(NEW_ANN_PATH, 'train', ann_fname))
for fname in tqdm(list_files_val):
ann_fname = fname.replace('.npy', '_ann.npy')
shutil.copy(os.path.join(ANN_PATH, ann_fname), os.path.join(NEW_ANN_PATH, 'val', ann_fname))
for fname in tqdm(list_files_test):
ann_fname = fname.replace('.npy', '_ann.npy')
shutil.copy(os.path.join(ANN_PATH, ann_fname), os.path.join(NEW_ANN_PATH, 'test', ann_fname))
AH_PATH = os.path.join(BASE_PATH, 'shhs1/ah_labels')
NEW_AH_PATH = os.path.join(BASE_PATH, 'shhs1/ah_labels_split')
os.makedirs(NEW_AH_PATH, exist_ok=True)
os.makedirs(os.path.join(NEW_AH_PATH,'train'), exist_ok=True)
os.makedirs(os.path.join(NEW_AH_PATH,'val'), exist_ok=True)
os.makedirs(os.path.join(NEW_AH_PATH,'test'), exist_ok=True)
for fname in tqdm(list_files_train):
ann_fname = fname.replace('.npy', '_ah.npy')
shutil.copy(os.path.join(AH_PATH, ann_fname), os.path.join(NEW_AH_PATH, 'train', ann_fname))
for fname in tqdm(list_files_val):
ann_fname = fname.replace('.npy', '_ah.npy')
shutil.copy(os.path.join(AH_PATH, ann_fname), os.path.join(NEW_AH_PATH, 'val', ann_fname))
for fname in tqdm(list_files_test):
ann_fname = fname.replace('.npy', '_ah.npy')
shutil.copy(os.path.join(AH_PATH, ann_fname), os.path.join(NEW_AH_PATH, 'test', ann_fname))
for split in ['train', 'val', 'test']:
# create link files to respiratory channels
os.makedirs(os.path.join(BASE_PATH, cfg.output_dir, split, 'RESP'), exist_ok=True)
for chn_grp in ['ABD', 'AIRFLOW']:
chns = [f.split('.')[0] for f in os.listdir(os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp))]
for chn in chns:
src_mmap = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.mmap')
src_pickle = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.pickle')
dst_mmap = os.path.join(BASE_PATH, cfg.output_dir, split, 'RESP', f'{chn}.mmap')
dst_pickle = os.path.join(BASE_PATH, cfg.output_dir, split, 'RESP', f'{chn}.pickle')
if not os.path.exists(dst_mmap):
os.symlink(src_mmap, dst_mmap)
if not os.path.exists(dst_pickle):
os.symlink(src_pickle, dst_pickle)
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