BPointExtractionDebski1993SecondDerivative

class pepbench.algorithms.icg.BPointExtractionDebski1993SecondDerivative(correct_outliers: bool = False, handle_missing_events: Literal['raise', 'warn', 'ignore'] = 'warn')

B-point extraction algorithm by Debski et al. (1993) based on the reversal of dZ^2/dt^2 before the C-point.

This algorithm extracts B-points based on the last reversal (local minimum) of the second derivative of the ICG signal before the C-point.

For more information, see [Deb93].

References

[Deb93]

Debski, T. T., Zhang, Y., Jennings, J. R., & Kamarck, T. W. (1993). Stability of cardiac impedance measures: Aortic opening (B-point) detection and scoring. Biological Psychology, 36(1-2), 63-74. https://doi.org/10.1016/0301-0511(93)90081-I

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__(correct_outliers: bool = False, handle_missing_events: Literal['raise', 'warn', 'ignore'] = 'warn')

Initialize new BPointExtractionDebski1993SecondDerivative instance.

Parameters:

correct_outliersbool, optional

Indicates whether to perform outlier correction (True) or not (False). Default: False

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

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

• “warn”: issue a warning and set the event to NaN

• “raise”: raise an EventExtractionError

• “ignore”: ignore the error and continue with the next event

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 reversal (local minimum) of the second derivative of the ICG signal 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=.