BPointExtractionArbol2017IsoelectricCrossings

class pepbench.algorithms.icg.BPointExtractionArbol2017IsoelectricCrossings(handle_missing_events: Literal['raise', 'warn', 'ignore'] = 'warn')

B-point extraction algorithm by Arbol et al. (2017) based on isoelectric crossings.

This algorithm extracts B-points based on the last crossing of the dZ/dt signal through the isoelectric line (i.e., the mean of the dZ/dt signal in the cardiac cycle) before the C-point.

For more information, see [Arb17].

Parameters:

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

How to handle failing event extraction. Must be one of:

• "warn": issue a warning and set the event to NaN,

• "raise": raise an EventExtractionError, or

• "ignore": continue silently.

Default: "warn".

Attributes:

points_BPointDataFrame

DataFrame containing the extracted B-points. Each row contains the B-point location (in samples from beginning of signal) for each heartbeat, index functions as id of heartbeat. B-point locations can be NaN if no B-points were detected for certain heartbeats.

References

[Arb17]

Árbol, J. R., Perakakis, P., Garrido, A., Mata, J. L., Fernández-Santaella, M. C., & Vila, J. (2017). Mathematical detection of aortic valve opening (B point) in impedance cardiography: A comparison of three popular algorithms. Psychophysiology, 54(3), 350-357. https://doi.org/10.1111/psyp.12799

Methods

clone()	Create a new instance of the class with all parameters copied over.
extract(*, icg, heartbeats, c_points, ...)	Extract B-points from given ICG derivative signal.
get_params([deep])	Get parameters for this algorithm.
set_params(**params)	Set the parameters of this Algorithm.

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

Initialize new BPointExtractionArbol2017IsoelectricCrossings algorithm instance.

Parameters:

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

How to handle failing event extraction. Must be one of:

• "warn": issue a warning and set the event to NaN,

• "raise": raise an EventExtractionError, or

• "ignore": continue silently.

Default: "warn".

extract(*, icg: _IcgRawDataFrame | DataFrame, heartbeats: _HeartbeatSegmentationDataFrame | DataFrame, c_points: _CPointDataFrame | DataFrame, sampling_rate_hz: float | None)

Extract B-points from given ICG derivative signal.

This algorithm extracts B-points based on the last isoelectric crossing before the C-point.

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

Parameters:

icgDataFrame

ICG derivative signal

heartbeatsDataFrame

Segmented heartbeats. Each row contains start, end, and R-peak location (in samples from beginning of signal) of that heartbeat, index functions as id of heartbeat

c_pointsDataFrame

Extracted C-points. Each row contains the C-point location (in samples from beginning of signal) for each heartbeat, index functions as id of heartbeat. C-point locations can be NaN if no C-points were detected for certain heartbeats

sampling_rate_hzint

sampling rate of ICG derivative signal in hz

Returns:

self

Raises:

EventExtractionError

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

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

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=.