ZScoreNormalize

class torch_ecg._preprocessors.ZScoreNormalize(mean: Union[numbers.Real, numpy.ndarray] = 0.0, std: Union[numbers.Real, numpy.ndarray] = 1.0, per_channel: bool = False, **kwargs: Any)[source]

Bases: torch_ecg._preprocessors.normalize.Normalize

Z-score normalization.

Z-score normalization defined as

\[\left(\frac{sig - \operatorname{mean}(sig)}{\operatorname{std}(sig)}\right) \cdot s + m\]
Parameters

  • mean (numbers.Real or numpy.ndarray, default 0.0) – Mean value of the normalized signal, or mean values for each lead of the normalized signal.

  • std (numbers.Real or numpy.ndarray, default 1.0) – Standard deviation of the normalized signal, or standard deviations for each lead of the normalized signal.

  • per_channel (bool, default False) – If True, normalization will be done per channel.

Examples

from torch_ecg.cfg import DEFAULTS
sig = DEFAULTS.RNG.randn(1000)
pp = ZScoreNormalize()
sig, _ = pp(sig, 500)
extra_repr_keys() → List[str][source]

Extra keys for __repr__() and __str__().