BandPass

class torch_ecg._preprocessors.BandPass(lowcut: Real | None = 0.5, highcut: Real | None = 45, filter_type: Literal['butter', 'fir'] = 'butter', filter_order: int | None = None, **kwargs: Any)

Bases: PreProcessor

Bandpass filtering preprocessor.

Parameters:

• lowcut (numbers.Real, optional) – Low cutoff frequency

• highcut (numbers.Real, optional) – High cutoff frequency.

• filter_type ({"butter", "fir"}, , default "butter") – Type of the bandpass filter.

• filter_order (int, optional) – Order of the bandpass filter.

• **kwargs (dict, optional) – Other arguments for PreProcessor.

Examples

from torch_ecg.cfg import DEFAULTS
sig = DEFAULTS.RNG.randn(1000)
pp = BandPass(lowcut=0.5, highcut=45, filter_type="butter", filter_order=4)
sig, _ = pp(sig, 500)

apply(sig: ndarray, fs: int) → 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 (int) – Sampling frequency of the ECG signal.

Returns:

• filtered_sig (numpy.ndarray) – Bandpass filtered ECG signal.

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

extra_repr_keys() → List[str]

Extra keys for __repr__() and __str__().