BaselineRemove

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

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, fs: Real) → Tuple[ndarray, int]

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 (numbers.Real) – Sampling frequency of the ECG signal.

Returns:

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

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

extra_repr_keys() → List[str]

Extra keys for __repr__() and __str__().