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 CSV file for the table or join is not found.

set_task(task=None, num_workers=None, input_processors=None, output_processors=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
        *.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.

Returns:

The generated sample dataset.

Return type:

SampleDataset

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]