QPeakExtractionVanLien2013

class pepbench.algorithms.ecg.QPeakExtractionVanLien2013(time_interval_ms: int = 40, handle_missing_events: Literal['raise', 'warn', 'ignore'] = 'warn')

Algorithm to extract Q-peaks based on the detection of the R-peak, as suggested by Van Lien et al. (2013).

The Q-peak is estimated by subtracting a fixed time interval from the R-peak location. The fixed time interval is defined by the parameter time_interval.

For more information on the algorithm, see [Van13].

References

[Van13]

Van Lien, R., Schutte, N. M., Meijer, J. H., & De Geus, E. J. C. (2013). Estimated preejection period (PEP) based on the detection of the R-peak and dZ/dt-min peaks does not adequately reflect the actual PEP across a wide range of laboratory and ambulatory conditions. International Journal of Psychophysiology, 87(1), 60-69. https://doi.org/10.1016/j.ijpsycho.2012.11.001

Methods

clone()	Create a new instance of the class with all parameters copied over.
extract(*, ecg, heartbeats, sampling_rate_hz)	Extract Q-peaks from given ECG signal.
get_params([deep])	Get parameters for this algorithm.
set_params(**params)	Set the parameters of this Algorithm.

__init__(time_interval_ms: int = 40, handle_missing_events: Literal['raise', 'warn', 'ignore'] = 'warn')

Initialize new QPeakExtractionVanLien2013 algorithm instance.

Parameters:

time_interval_msint, optional

Specify the constant time interval in milliseconds which will be subtracted from the R-peak for Q-peak estimation. Default: 40 ms

handle_missing_eventsone of {“warn”, “raise”, “ignore”}, optional

How to handle missing data in the input dataframes. Default: “warn”

clone() → Self

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

extract(*, ecg: _EcgRawDataFrame | DataFrame | None, heartbeats: _HeartbeatSegmentationDataFrame | DataFrame, sampling_rate_hz: float)

Extract Q-peaks from given ECG signal.

The results are saved in the points_ attribute of the super class.

Parameters:

ecg: :class:`~pandas.DataFrame`

ECG signal. Not used in this function since Q-peak is estimated from the R-peaks in the heartbeats DataFrame.

heartbeats: :class:`~pandas.DataFrame`

DataFrame containing one row per segmented heartbeat, each row contains start, end, and R-peak location (in samples from beginning of signal) of that heartbeat, index functions as id of heartbeat

sampling_rate_hz: int

Sampling rate of ECG signal in hz

Returns:

self

Raises:

EventExtractionError

If the event extraction fails and handle_missing is set to “raise”

get_params(deep: bool = True) → dict[str, Any]

Get parameters for this algorithm.

Parameters:

deep

Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params

Parameter names mapped to their values.

set_params(**params: Any) → Self

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.