pyhealth.datasets.TUABDataset#

Dataset is available at https://isip.piconepress.com/projects/nedc/html/tuh_eeg/#c_tuab

The TUAB dataset (or Temple University Hospital EEG Abnormal Corpus) is a collection of EEG data acquired at the Temple University Hospital.

The dataset contains both normal and abnormal EEG readings.

class pyhealth.datasets.TUABDataset(root, dataset_name=None, config_path=None, subset='both', **kwargs)[source]#

Bases: BaseDataset

Base EEG dataset for the TUH Abnormal EEG Corpus

Dataset is available at https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml

The TUAB dataset (or Temple University Hospital EEG Abnormal Corpus) is a collection of EEG data acquired at the Temple University Hospital.

The dataset contains both normal and abnormal EEG readings.

Files are named in the form aaaaamye_s001_t000.edf. This includes the subject identifier (“aaaaamye”), the session number (“s001”) and a token number (“t000”). EEGs are split into a series of files starting with *t000.edf, *t001.edf, …

Parameters:

dataset_name (Optional[str]) – name of the dataset.
root (str) – root directory of the raw data. You can choose to use the path to Cassette portion or the Telemetry portion.
dev – whether to enable dev mode (only use a small subset of the data). Default is False.
refresh_cache – whether to refresh the cache; if true, the dataset will be processed from scratch and the cache will be updated. Default is False.

task#: Optional[str], name of the task (e.g., “EEG_abnormal”). Default is None.

samples#: Optional[List[Dict]], a list of samples, each sample is a dict with patient_id, record_id, and other task-specific attributes as key. Default is None.

patient_to_index#: Optional[Dict[str, List[int]]], a dict mapping patient_id to a list of sample indices. Default is None.

visit_to_index#: Optional[Dict[str, List[int]]], a dict mapping visit_id to a list of sample indices. Default is None.

Examples

>>> from pyhealth.datasets import TUABDataset
>>> dataset = TUABDataset(
...         root="/srv/local/data/TUH/tuh_eeg_abnormal/v3.0.0/edf/",
...     )
>>> dataset.stat()
>>> dataset.info()

prepare_metadata()[source]#

Build and save processed metadata CSVs for TUAB train/eval separately.

This writes: - <root>/tuab-train-pyhealth.csv - <root>/tuab-eval-pyhealth.csv

Train and eval filenames look like: aaaaalkt_s001_t000.edf - subject_id = aaaaalkt - session_id = s001 - token_id = t000

We define record_id as session_id + token_id.

The label is derived from the directory: - abnormal -> 1 - normal -> 0

Return type:: None

property default_task: EEGAbnormalTUAB#

EEGAbnormalTUAB.

Returns:: The default task instance.
Return type:: EEGAbnormalTUAB
Type:: Returns the default task for the TUAB dataset

clean_tmpdir()#

Cleans up the temporary directory within the cache.

Return type:: None

create_tmpdir()#

Creates and returns a new temporary directory within the cache.

Returns:: The path to the new temporary directory.
Return type:: Path

get_patient(patient_id)#

Retrieves a Patient object for the given patient ID.

Parameters:: patient_id (str) – The ID of the patient to retrieve.
Returns:: The Patient object for the given ID.
Return type:: Patient
Raises:: AssertionError – If the patient ID is not found in the dataset.

property global_event_df: LazyFrame#

Returns the path to the cached event dataframe.

Returns:: The path to the cached event dataframe.
Return type:: Path

iter_patients(df=None)#

Yields Patient objects for each unique patient in the dataset.

Yields:: Iterator[Patient] – An iterator over Patient objects.
Return type:: Iterator[Patient]

load_data()#

Loads data from the specified tables.

Returns:: A concatenated lazy frame of all tables.
Return type:: dd.DataFrame

load_table(table_name)#

Loads a table and processes joins if specified.

Parameters:

table_name (str) – The name of the table to load.

Returns:

The processed Dask dataframe for the table.

Return type:

dd.DataFrame

Raises:

ValueError – If the table is not found in the config.
FileNotFoundError – If the source file (CSV/TSV or Parquet) for the table or join is not found.

set_task(task=None, num_workers=None, input_processors=None, output_processors=None, split=None)#

Processes the base dataset to generate the task-specific sample dataset. The cache structure is as follows:

{task_name}_{task_uuid}/        # Cached data for specific task based on task name, schema, and args
    task_df.ld/                 # Intermediate task dataframe based on schema
    samples_{proc_uuid}.ld/     # Final processed samples after applying processors
        schema.pkl              # Saved SampleBuilder schema
        split.npz               # Sample indices per split part (with split= only)
        *.bin                   # Processed sample files

Parameters:

task (Optional[BaseTask]) – The task to set. Uses default task if None.
num_workers (int) – Number of workers for multi-threading. Default is self.num_workers.
input_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted input processors. If provided, these will be used instead of creating new ones from task’s input_schema. Defaults to None.
output_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted output processors. If provided, these will be used instead of creating new ones from task’s output_schema. Defaults to None.
split (Optional[Split]) – Split the samples, e.g. by patient with PatientSplit, and fit every processor on the first (training) part only, so the other parts never shape preprocessing. The parts are used as the split returns them. Samples are streamed; nothing is loaded into memory beyond the per-sample index that processing already keeps. Defaults to None: fit on all samples and return one dataset, as before.

Returns:

The generated sample dataset, or, with split, a tuple with one dataset per part, training part first, whose processors were fitted on the training part.

Return type:

SampleDataset

Examples

>>> from pyhealth.datasets import PatientSplit
>>> train, val, test = dataset.set_task(  
...     task, split=PatientSplit(ratios=(0.7, 0.1, 0.2), seed=42)
... )

Raises:: AssertionError – If no default task is found and task is None.

stats()#

Prints statistics about the dataset.

Return type:: None

property unique_patient_ids: List[str]#

Returns a list of unique patient IDs.

Returns:: List of unique patient IDs.
Return type:: List[str]