MITDBDataset

class torch_ecg.databases.datasets.MITDBDataset(config: CFG, task: str, training: bool = True, lazy: bool = True, **reader_kwargs: Any)

Bases: ReprMixin, Dataset

Data generator for feeding data into pytorch models using the MITDB database.

Parameters:

• config (dict) –

  Configurations for the dataset, ref. MITDBDataset. A simple example is as follows:

  >>> config = deepcopy(MITDBDataset)
  >>> config.db_dir = "some/path/to/db"
  >>> dataset = MITDBDataset(config, task="qrs_detection", training=True, lazy=False)
  

• training (bool, default True) – If True, the training set will be loaded, otherwise the test (val) set will be loaded.

• lazy (bool, default True) – If True, the data will not be loaded immediately, instead, it will be loaded on demand.

• **reader_kwargs (dict, optional) – Keyword arguments for the database reader class.

extra_repr_keys() → List[str]

Extra keys for __repr__() and __str__().

persistence(force_recompute: bool = False, verbose: int = 0) → None

Save the preprocessed data to disk.

Parameters:

• force_recompute (bool, default False) – Whether to force recompute the preprocessed data.

• verbose (int, default 0) – Verbosity level for printing the progress.

Return type:

None

plot_seg(seg: str, ticks_granularity: int = 0) → None

Plot the segment.

Parameters:

• seg (str) – Name of the segment, of pattern like “S_1_1_0000193”.

• ticks_granularity (int, default 0) – Granularity to plot axis ticks, the higher the more ticks. 0 (no ticks) –> 1 (major ticks) –> 2 (major + minor ticks)

Return type:

None

reset_task(task: str, lazy: bool = True) → None

Reset the task of the data generator.

Parameters:

• task (str) – The task to be set.

• lazy (bool, optional) – Whether to load the data lazily, by default True.

Return type:

None