pyhealth.tasks.sleep_staging#

pyhealth.tasks.sleep_staging.sleep_staging_isruc_fn(record, epoch_seconds=10, label_id=1)[source]#

Processes a single patient for the sleep staging task on ISRUC.

Sleep staging aims at predicting the sleep stages (Awake, N1, N2, N3, REM) based on the multichannel EEG signals. The task is defined as a multi-class classification.

Parameters

  • record –

    a singleton list of one subject from the ISRUCDataset. The (single) record is a dictionary with the following keys:

    load_from_path, signal_file, label1_file, label2_file, save_to_path, subject_id

  • epoch_seconds – how long will each epoch be (in seconds). It has to be a factor of 30 because the original data was labeled every 30 seconds.

  • label_id – which set of labels to use. ISURC is labeled by two experts. By default we use the first set of labels (label_id=1).

Returns

a list of samples, each sample is a dict with patient_id, record_id,

and epoch_path (the path to the saved epoch {“X”: signal, “Y”: label} as key.

Return type

samples

Note that we define the task as a multi-class classification task.

Examples

>>> from pyhealth.datasets import ISRUCDataset
>>> isruc = ISRUCDataset(
...         root="/srv/local/data/data/ISRUC-I", download=True,
...     )
>>> from pyhealth.tasks import sleep_staging_isruc_fn
>>> sleepstage_ds = isruc.set_task(sleep_staging_isruc_fn)
>>> sleepstage_ds.samples[0]
{
    'record_id': '1-0',
    'patient_id': '1',
    'epoch_path': '/home/zhenlin4/.cache/pyhealth/datasets/832afe6e6e8a5c9ea5505b47e7af8125/10-1/1/0.pkl',
    'label': 'W'
}
pyhealth.tasks.sleep_staging.sleep_staging_sleepedf_fn(record, epoch_seconds=30)[source]#

Processes a single patient for the sleep staging task on Sleep EDF.

Sleep staging aims at predicting the sleep stages (Awake, REM, N1, N2, N3, N4) based on the multichannel EEG signals. The task is defined as a multi-class classification.

Parameters

  • patient – a list of (load_from_path, signal_file, label_file, save_to_path) tuples, where PSG is the signal files and the labels are

  • file (in label) –

  • epoch_seconds – how long will each epoch be (in seconds)

Returns

a list of samples, each sample is a dict with patient_id, record_id,

and epoch_path (the path to the saved epoch {“X”: signal, “Y”: label} as key.

Return type

samples

Note that we define the task as a multi-class classification task.

Examples

>>> from pyhealth.datasets import SleepEDFDataset
>>> sleepedf = SleepEDFDataset(
...         root="/srv/local/data/SLEEPEDF/sleep-edf-database-expanded-1.0.0/sleep-cassette",
...     )
>>> from pyhealth.tasks import sleep_staging_sleepedf_fn
>>> sleepstage_ds = sleepedf.set_task(sleep_staging_sleepedf_fn)
>>> sleepstage_ds.samples[0]
{
    'record_id': 'SC4001-0',
    'patient_id': 'SC4001',
    'epoch_path': '/home/chaoqiy2/.cache/pyhealth/datasets/70d6dbb28bd81bab27ae2f271b2cbb0f/SC4001-0.pkl',
    'label': 'W'
}
pyhealth.tasks.sleep_staging.sleep_staging_shhs_fn(record, epoch_seconds=30)[source]#

Processes a single recording for the sleep staging task on SHHS.

Sleep staging aims at predicting the sleep stages (Awake, REM, N1, N2, N3) based on the multichannel EEG signals. The task is defined as a multi-class classification.

Parameters

  • patient – a list of (load_from_path, signal file, label file, save_to_path) tuples, where the signal is in edf file and

  • file (the labels are in the label) –

  • epoch_seconds – how long will each epoch be (in seconds), 30 seconds as default given by the label file

Returns

a list of samples, each sample is a dict with patient_id, record_id,

and epoch_path (the path to the saved epoch {“X”: signal, “Y”: label} as key.

Return type

samples

Note that we define the task as a multi-class classification task.

Examples

>>> from pyhealth.datasets import SHHSDataset
>>> shhs = SleepEDFDataset(
...         root="/srv/local/data/SHHS/polysomnography",
...         dev=True,
...     )
>>> from pyhealth.tasks import sleep_staging_shhs_fn
>>> shhs_ds = sleepedf.set_task(sleep_staging_shhs_fn)
>>> shhs_ds.samples[0]
{
    'record_id': 'shhs1-200001-0',
    'patient_id': 'shhs1-200001',
    'epoch_path': '/home/chaoqiy2/.cache/pyhealth/datasets/76c1ce8195a2e1a654e061cb5df4671a/shhs1-200001-0.pkl',
    'label': '0'
}