File size: 6,871 Bytes
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import os
import numpy as np
from tqdm import tqdm
import multiprocessing as mp
root = "/data/datasets/BigDownstream/chb-mit/processed"
out = "/data/datasets/BigDownstream/chb-mit/processed_seg"
# root = 'clean_signals'
# out = 'clean_segments'
if not os.path.exists(out):
os.makedirs(out)
# dump chb23 and chb24 to test, ch21 and ch22 to val, and the rest to train
test_pats = ["chb23", "chb24"]
val_pats = ["chb21", "chb22"]
train_pats = [
"chb01",
"chb02",
"chb03",
"chb04",
"chb05",
"chb06",
"chb07",
"chb08",
"chb09",
"chb10",
"chb11",
"chb12",
"chb13",
"chb14",
"chb15",
"chb16",
"chb17",
"chb18",
"chb19",
"chb20",
]
channels = [
"FP1-F7",
"F7-T7",
"T7-P7",
"P7-O1",
"FP2-F8",
"F8-T8",
"T8-P8",
"P8-O2",
"FP1-F3",
"F3-C3",
"C3-P3",
"P3-O1",
"FP2-F4",
"F4-C4",
"C4-P4",
"P4-O2",
]
SAMPLING_RATE = 256
def sub_to_segments(folder, out_folder):
print(f"Processing {folder}...")
# each recording
for f in tqdm(os.listdir(os.path.join(root, folder))):
print(f"Processing {folder}/{f}...")
record = pickle.load(open(os.path.join(root, folder, f), "rb"))
"""
{'FP1-F7': array([-145.93406593, 0.1953602 , 0.1953602 , ..., -11.52625153, -2.93040293, 19.34065934]),
'F7-T7': array([-104.51770452, 0.1953602 , 0.1953602 , ..., 23.63858364, 27.54578755, 30.67155067]),
'T7-P7': array([-42.78388278, 0.1953602 , 0.1953602 , ..., 48.64468864, 45.12820513, 34.57875458]),
'P7-O1': array([-33.01587302, 0.1953602 , 0.1953602 , ..., -17.77777778, -20.51282051, -25.59218559]),
'FP1-F3': array([-170.94017094, 0.1953602 , 0.1953602 , ..., -34.96947497, -25.98290598, 0.1953602 ]),
'F3-C3': array([-110.76923077, 0.1953602 , 0.1953602 , ..., 38.0952381 , 48.64468864, 50.20757021]),
'C3-P3': array([11.91697192, 0.1953602 , 0.1953602 , ..., 40.04884005, 33.7973138 , 25.98290598]),
'P3-O1': array([-56.45909646, 0.1953602 , 0.1953602 , ..., 0.97680098, -6.44688645, -16.60561661]),
'FP2-F4': array([-139.29181929, 0.1953602 , 0.1953602 , ..., -2.14896215, -2.14896215, -0.58608059]),
'F4-C4': array([-1.36752137, 0.1953602 , 0.1953602 , ..., 1.75824176, 2.93040293, 7.22832723]),
'C4-P4': array([63.88278388, 0.1953602 , 0.1953602 , ..., 16.996337 , 23.63858364, 25.59218559]),
'P4-O2': array([-14.26129426, 0.1953602 , 0.1953602 , ..., -13.08913309, -8.00976801, -13.47985348]),
'FP2-F8': array([-2.67838828e+02, 1.95360195e-01, 1.95360195e-01, ..., 6.83760684e+00, 6.05616606e+00, 6.44688645e+00]),
'F8-T8': array([ 57.24053724, 0.1953602 , 0.1953602 , ..., -2.53968254, -9.96336996, -12.6984127 ]),
'T8-P8': array([44.73748474, 0.1953602 , 0.1953602 , ..., 16.996337 , 22.46642247, 26.37362637]),
'P8-O2': array([ 74.82295482, 0.1953602 , -0.1953602 , ..., -17.38705739, -1.75824176, -2.53968254]),
'FZ-CZ': array([-106.08058608, 0.1953602 , 0.1953602 , ..., 24.81074481, 28.71794872, 28.71794872]),
'CZ-PZ': array([84.59096459, 0.1953602 , 0.1953602 , ..., 18.94993895, 20.51282051, 18.16849817]),
'P7-T7': array([ 43.17460317, 0.1953602 , 0.1953602 , ..., -48.25396825, -44.73748474, -34.18803419]),
'T7-FT9': array([-57.24053724, 0.1953602 , 0.1953602 , ..., -11.91697192, -3.71184371, 2.14896215]),
'FT9-FT10': array([-2.64713065e+02, 1.95360195e-01, 5.86080586e-01, ..., 9.76800977e-01, -1.58241758e+01, -2.94993895e+01]),
'FT10-T8': array([ 94.74969475, 0.1953602 , 0.1953602 , ..., -7.22832723, -10.35409035, -13.47985348]),
'T8-P8-2': array([44.73748474, 0.1953602 , 0.1953602 , ..., 16.996337 , 22.46642247, 26.37362637]),
'metadata': {'seizures': 0, 'times': [], 'channels': ['FP1-F7', 'F7-T7', 'T7-P7', 'P7-O1', 'FP1-F3', 'F3-C3', 'C3-P3', 'P3-O1', 'FP2-F4', 'F4-C4', 'C4-P4', 'P4-O2', 'FP2-F8', 'F8-T8', 'T8-P8', 'P8-O2', 'FZ-CZ', 'CZ-PZ', 'P7-T7', 'T7-FT9', 'FT9-FT10', 'FT10-T8', 'T8-P8-2']}}
"""
signal = []
for channel in channels:
if channel in record:
signal.append(record[channel])
else:
raise ValueError(f"Channel {channel} not found in record {record}")
signal = np.array(signal)
if "times" in record["metadata"]:
seizure_times = record["metadata"]["times"]
else:
seizure_times = []
# split the signal into segments on the second dimension by SAMPLING_RATE * 10 seconds
for i in range(0, signal.shape[1], SAMPLING_RATE * 10):
segment = signal[:, i : i + 10 * SAMPLING_RATE]
if segment.shape[1] == 10 * SAMPLING_RATE:
# judge whether the segment contains seizures
label = 0
for seizure_time in seizure_times:
if (
i < seizure_time[0] < i + 10 * SAMPLING_RATE
or i < seizure_time[1] < i + 10 * SAMPLING_RATE
):
label = 1
break
# save the segment
pickle.dump(
{"X": segment, "y": label},
open(
os.path.join(out_folder, f"{f.split('.')[0]}-{i}.pkl"),
"wb",
),
)
for idx, seizure_time in enumerate(seizure_times):
for i in range(
max(0, seizure_time[0] - SAMPLING_RATE),
min(seizure_time[1] + SAMPLING_RATE, signal.shape[1]),
5 * SAMPLING_RATE,
):
segment = signal[:, i : i + 10 * SAMPLING_RATE]
label = 1
# save the segment
pickle.dump(
{"X": segment, "y": label},
open(
os.path.join(
out_folder, f"{f.split('.')[0]}-s-{idx}-add-{i}.pkl"
),
"wb",
),
)
# parallel parameters
folders = os.listdir(root)
out_folders = []
for folder in folders:
if folder in test_pats:
out_folder = os.path.join(out, "test")
elif folder in val_pats:
out_folder = os.path.join(out, "val")
else:
out_folder = os.path.join(out, "train")
if not os.path.exists(out_folder):
os.makedirs(out_folder)
out_folders.append(out_folder)
# process in parallel
with mp.Pool(mp.cpu_count()) as pool:
res = pool.starmap(sub_to_segments, zip(folders, out_folders))
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