def test_crop_wake():
sp = SleepPhysionet(subject_ids=[0],
recording_ids=[1],
preload=True,
load_eeg_only=True,
crop_wake_mins=30)
sfreq = sp.datasets[0].raw.info['sfreq']
duration_h = len(sp) / (3600 * sfreq)
assert duration_h < 7 and duration_h > 6
def get_sleep_physionet(subject_ids=range(83), recording_ids=[1, 2]):
bug_subjects = [13, 36, 39, 48, 52, 59, 65, 68, 69, 73, 75, 76, 78, 79]
subject_ids = [id_ for id_ in subject_ids if id_ not in bug_subjects]
dataset = SleepPhysionet(subject_ids=subject_ids,
recording_ids=recording_ids,
crop_wake_mins=30)
high_cut_hz = 30
preprocessors = [
# convert from volt to microvolt, directly modifying the numpy array
NumpyPreproc(fn=lambda x: x * 1e6),
# bandpass filter
MNEPreproc(fn='filter', l_freq=None, h_freq=high_cut_hz),
]
# Transform the data
preprocess(dataset, preprocessors)
mapping = { # We merge stages 3 and 4 following AASM standards.
'Sleep stage W': 0,
'Sleep stage 1': 1,
'Sleep stage 2': 2,
'Sleep stage 3': 3,
'Sleep stage 4': 3,
'Sleep stage R': 4
}
window_size_s = 30
sfreq = 100
window_size_samples = window_size_s * sfreq
windows_dataset = create_windows_from_events(
dataset,
trial_start_offset_samples=0,
trial_stop_offset_samples=0,
window_size_samples=window_size_samples,
window_stride_samples=window_size_samples,
preload=True,
mapping=mapping)
preprocess(windows_dataset, [MNEPreproc(fn=zscore)])
info = pd.read_excel('SC-subjects.xls')
return windows_dataset, info
# train our network and the second one to evaluate performance (as in the `MNE`_
# sleep staging example).
#
# .. _MNE: https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html
#
# .. note::
# To load your own datasets either via MNE or from
# preprocessed X/y numpy arrays, see the `MNE Dataset
# Tutorial <./plot_mne_dataset_example.html>`__ and the `Numpy Dataset
# Tutorial <./plot_custom_dataset_example.html>`__.
#
from braindecode.datasets.sleep_physionet import SleepPhysionet
dataset = SleepPhysionet(subject_ids=[0, 1],
recording_ids=[1],
crop_wake_mins=30)
######################################################################
# Preprocessing
# ~~~~~~~~~~~~~
#
######################################################################
# Next, we preprocess the raw data. We apply convert the data to microvolts and
# apply a lowpass filter. We omit the downsampling step of [1]_ as the Sleep
# Physionet data is already sampled at a lower 100 Hz.
#
from braindecode.datautil.preprocess import (MNEPreproc, NumpyPreproc,
preprocess)
def test_sleep_physionet():
sp = SleepPhysionet(subject_ids=[0],
recording_ids=[1],
preload=True,
load_eeg_only=True)
assert isinstance(sp, BaseConcatDataset)
def test_all_signals():
sp = SleepPhysionet(subject_ids=[0],
recording_ids=[1],
preload=True,
load_eeg_only=False)
assert len(sp.datasets[0].raw.ch_names) == 7