WaveDelineationMetrics

class torch_ecg.components.WaveDelineationMetrics(macro: bool = True, tol: float = 0.15, extra_metrics: Callable | None = None)

Bases: Metrics

Metrics for the task of ECG wave delineation.

Parameters:

• macro (bool, default True) – Whether to use macro-averaged metrics or not.

• tol (float, default 0.15) – Tolerance for the duration of the waveform, with units in seconds.

• extra_metrics (callable, optional) –

  Extra metrics to compute, has to be a function with signature

  def extra_metrics(
      labels: Sequence[Union[Sequence[int], np.ndarray]],
      outputs: Sequence[Union[Sequence[int], np.ndarray]],
      fs: int
  ) -> dict
  

compute(labels: ndarray | Tensor, outputs: ndarray | Tensor, class_map: Dict[str, int], fs: int, mask_format: str = 'channel_first', tol: float | None = None) → WaveDelineationMetrics

Compute metrics for the task of ECG wave delineation (sensitivity, precision, f1_score, mean error and standard deviation of the mean errors) for multiple evaluations.

Parameters:

• labels (numpy.ndarray or torch.Tensor) – Ground truth masks, of shape (n_samples, n_channels, n_timesteps).

• outputs (numpy.ndarray or torch.Tensor) – Predictions corresponding to labels, of the same shape.

• class_map (dict) – Class map, mapping names to waves to numbers from 0 to n_classes-1, the keys should contain “pwave”, “qrs”, “twave”.

• fs (numbers.Real) – Sampling frequency of the signal corresponding to the masks, used to compute the duration of each waveform, and thus the error and standard deviations of errors.

• mask_format (str, default "channel_first") – Format of the mask, one of the following: “channel_last” (alias “lead_last”), or “channel_first” (alias “lead_first”).

• tol (float, optional) – Tolerance for the duration of the waveform, with units in seconds. Defaults to self.tol.

Returns:

self – The metrics object itself with the computed metrics.

Return type:

WaveDelineationMetrics

extra_repr_keys() → List[str]

Extra keys for __repr__() and __str__().

set_macro(macro: bool) → None

Set whether to use macro-averaged metrics or not.

Parameters:

macro (bool) – Shether to use macro-averaged metrics.