BaselineRemove#

class torch_ecg._preprocessors.BaselineRemove(window1: float = 0.2, window2: float = 0.6, **kwargs: Any)[source]#

Bases: PreProcessor

Baseline removal using median filter.

Parameters:

  • window1 (float, default 0.2) – The smaller window size of the median filter, with units in seconds.

  • highcut (float, default 0.6) – The larger window size of the median filter, with units in seconds.

Examples

from torch_ecg.cfg import DEFAULTS
sig = DEFAULTS.RNG.randn(1000)
pp = BaselineRemove(window1=0.2, window2=0.6)
sig, _ = pp(sig, 500)
apply(sig: ndarray[tuple[Any, ...], dtype[_ScalarT]], fs: float | int) → Tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], int | float][source]#

Apply the preprocessor to sig.

Parameters:

  • sig (numpy.ndarray) – The ECG signal, can be - 1d array, which is a single-lead ECG; - 2d array, which is a multi-lead ECG of “lead_first” format; - 3d array, which is a tensor of several ECGs, of shape (batch, lead, siglen).

  • fs (float or int) – Sampling frequency of the ECG signal.

Returns:

  • filtered_sig (numpy.ndarray) – The median filtered (hence baseline removed) ECG signal.

  • fs (float or int) – Sampling frequency of the filtered ECG signal.

extra_repr_keys() → List[str][source]#

Extra keys for __repr__() and __str__().