ECG_SUBTRACT_UNET

class torch_ecg.models.ECG_SUBTRACT_UNET(classes: Sequence[str], n_leads: int, config: Optional[torch_ecg.cfg.CFG] = None)[source]

Bases: torch.nn.modules.module.Module, torch_ecg.utils.utils_nn.CkptMixin, torch_ecg.utils.utils_nn.SizeMixin

U-Net for ECG wave delineation.

Entry 0433 of CPSC2019, which is a modification of the U-Net using subtraction instead of addition in branched bottom block.

Parameters

  • classes (Sequence[str]) – List of names of the classes.

  • n_leads (int) – Number of input leads (number of input channels).

  • config (CFG, optional) – Other hyper-parameters, including kernel sizes, etc. Refer to the corresponding config file.

compute_output_shape(seq_len: Optional[int] = None, batch_size: Optional[int] = None) → Sequence[Optional[int]][source]

Compute the output shape of the model.

Parameters

  • seq_len (int, optional) – Length of the input signal tensor.

  • batch_size (int, optional) – Batch size of the input signal tensor.

Returns

output_shape – Output shape of the model.

Return type

sequence

forward(input: torch.Tensor) → torch.Tensor[source]

Forward pass of the model.

Parameters

input (torch.Tensor) – Input signal tensor, of shape (batch_size, n_channels, seq_len).

Returns

output – Output tensor, of shape (batch_size, n_channels, seq_len).

Return type

torch.Tensor

inference(input: Union[numpy.ndarray, torch.Tensor], bin_pred_thr: float = 0.5) → torch.Tensor[source]

Method for making inference on a single input.