RR_LSTM

class torch_ecg.models.RR_LSTM(classes: Sequence[str], config: CFG | None = None, **kwargs: Any)

Bases: Module, CkptMixin, SizeMixin, CitationMixin

LSTM model for RR time series classification or sequence labeling.

LSTM model using RR time series as input is studied in [Faust et al.[1]] for atrial fibrillation detection. It is further improved in [Wen et al.[2]] via incorporating attention mechanism and conditional random fields.

Parameters:

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

• config (dict) – Other hyper-parameters, including kernel sizes, etc. Refer to corresponding config file for details.

References

[1]

Oliver Faust, Alex Shenfield, Murtadha Kareem, Tan Ru San, Hamido Fujita, and U Rajendra Acharya. Automated Detection of Atrial Fibrillation using Long Short-Term Memory Network with RR Interval Signals. Computers in Biology and Medicine, 102:327–335, 11 2018. doi:10.1016/j.compbiomed.2018.07.001.

[2]

Hao Wen, Wenjian Yu, Yuanqing Wu, Shuai Yang, and Xiaolong Liu. A Scalable Hybrid Model for Atrial Fibrillation Detection. Journal of Mechanics in Medicine and Biology, 21(05):2140021, 4 2021. doi:10.1142/s0219519421400212.

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

Compute the output shape of the model.

Parameters:

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

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

Returns:

output_shape – Output shape of the model.

Return type:

sequence

forward(input: Tensor) → Tensor

Forward pass of the model.

Parameters:

input (torch.Tensor) – Input RR series tensor of shape (seq_len, batch_size, n_channels), or (batch_size, n_channels, seq_len) if config.batch_first is True.

Returns:

Output tensor, of shape (batch_size, seq_len, n_classes) or (batch_size, n_classes).

Return type:

torch.Tensor

classmethod from_v1(v1_ckpt: str, device: device | None = None, return_config: bool = False) → RR_LSTM | Tuple[RR_LSTM, dict]

Restore an instance of the model from a v1 checkpoint.

Parameters:

• v1_ckpt (str) – Path to the v1 checkpoint file.

• device (torch.device, optional) – The device to load the model to. Defaults to “cuda” if available, otherwise “cpu”.

• return_config (bool, default False) – Whether to return the config dict.

Returns:

model – The model instance restored from the v1 checkpoint.

Return type:

RR_LSTM

inference(input: Tensor, bin_pred_thr: float = 0.5) → BaseOutput

Inference method for the model.