Source code for biopsykit.signals.icg.event_extraction._b_point_lozano2007
import numpy as np
import pandas as pd
from tpcp import Parameter
from biopsykit.signals._base_extraction import HANDLE_MISSING_EVENTS, CanHandleMissingEventsMixin
from biopsykit.signals.icg.event_extraction._base_b_point_extraction import BaseBPointExtraction
__all__ = ["BPointExtractionLozano2007LinearRegression", "BPointExtractionLozano2007QuadraticRegression"]
from biopsykit.utils.dtypes import (
CPointDataFrame,
HeartbeatSegmentationDataFrame,
IcgRawDataFrame,
is_b_point_dataframe,
is_c_point_dataframe,
is_heartbeat_segmentation_dataframe,
is_icg_raw_dataframe,
)
[docs]class BPointExtractionLozano2007LinearRegression(BaseBPointExtraction, CanHandleMissingEventsMixin):
"""B-point extraction algorithm by Lozano et al. (2007) based on linear regression of R-C interval.
This algorithm extracts B-points based on the linear regression of the relationship between the R-C interval
and the B-point.
For more information, see [Loz07]_.
References
----------
.. [Loz07] Lozano, D. L., Norman, G., Knox, D., Wood, B. L., Miller, B. D., Emery, C. F., & Berntson, G. G. (2007).
Where to B in dZ/dt. Psychophysiology, 44(1), 113-119. https://doi.org/10.1111/j.1469-8986.2006.00468.x
"""
# input parameters
moving_average_window: Parameter[int]
def __init__(self, moving_average_window: int = 1, handle_missing_events: HANDLE_MISSING_EVENTS = "warn"):
"""Initialize new ``BPointExtractionLozano2007LinearRegression`` instance.
Parameters
----------
moving_average_window : int, optional
Window size for moving average filter (in heartbeats, centered around the current heartbeat)
to compute the R-C interval. Default: 1 (no moving average).
handle_missing_events : one 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"``.
"""
super().__init__(handle_missing_events=handle_missing_events)
self.moving_average_window = moving_average_window
[docs] def extract(
self,
*,
icg: IcgRawDataFrame,
heartbeats: HeartbeatSegmentationDataFrame,
c_points: CPointDataFrame,
sampling_rate_hz: float,
):
"""Extract B-points from given ICG derivative signal.
This algorithm extracts B-points using linear regression based on the relationship between
the R-C interval and the B-point.
The results are saved in the ``points_`` attribute of the super class.
Parameters
----------
icg : :class:`~pandas.DataFrame`
ICG derivative signal
heartbeats : :class:`~pandas.DataFrame`
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_points : :class:`~pandas.DataFrame`
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_hz : int
sampling rate of ICG derivative signal in hz
Returns
-------
self
Raises
------
:exc:`~biopsykit.utils.exceptions.EventExtractionError`
If the event extraction fails and ``handle_missing`` is set to "raise"
"""
self._check_valid_missing_handling()
is_icg_raw_dataframe(icg)
is_heartbeat_segmentation_dataframe(heartbeats)
is_c_point_dataframe(c_points)
# result dfs
b_points = pd.DataFrame(index=heartbeats.index, columns=["b_point_sample", "nan_reason"])
# used subsequently to store ids of heartbeats where no B was detected because there was no C
# (Bs should always be found, since they are set to the max of the 3rd derivative, and there is always a max)
heartbeats_no_c_b = []
# search B-point for each heartbeat of the given signal
for idx, _data in heartbeats.iterrows():
if self.moving_average_window == 1:
c_point_sample = c_points.loc[[idx], "c_point_sample"]
r_peak_sample = heartbeats.loc[[idx], "r_peak_sample"]
else:
window_width = self.moving_average_window // 2
start_idx = heartbeats.index[max(0, idx - window_width)]
end_idx = heartbeats.index[min(len(heartbeats) - 1, idx + window_width + 1)]
c_point_sample = c_points.loc[start_idx:end_idx, "c_point_sample"].dropna()
r_peak_sample = heartbeats.loc[start_idx:end_idx, "r_peak_sample"].dropna()
# C-point can be NaN, then, extraction of B is not possible, so B is set to NaN
if pd.isna(c_point_sample).any():
heartbeats_no_c_b.append(idx)
b_points.loc[idx, "b_point_sample"] = np.nan
b_points.loc[idx, "nan_reason"] = "c_point_nan"
continue
current_r_peak = heartbeats.loc[idx, "r_peak_sample"]
# get the R-C interval in ms
r_c_interval_ms = np.mean((c_point_sample - r_peak_sample) / sampling_rate_hz * 1000)
if pd.isna(r_c_interval_ms):
b_points.loc[idx, "b_point_sample"] = np.nan
b_points.loc[idx, "nan_reason"] = "no_r_c_interval"
continue
b_point_interval_ms = 0.55 * r_c_interval_ms + 4.45
b_point_interval_sample = int((b_point_interval_ms * sampling_rate_hz) / 1000)
b_point_sample = current_r_peak + b_point_interval_sample
b_points.loc[idx, "b_point_sample"] = b_point_sample
b_points = b_points.astype({"b_point_sample": "Int64", "nan_reason": "object"})
is_b_point_dataframe(b_points)
self.points_ = b_points
return self
[docs]class BPointExtractionLozano2007QuadraticRegression(BaseBPointExtraction, CanHandleMissingEventsMixin):
"""B-point extraction algorithm by Lozano et al. (2007) [1]_ based on quadratic regression of R-C interval.
This algorithm extracts B-points based on the quadratic regression of the relationship between the R-C interval
and the B-point.
References
----------
.. [1] Lozano, D. L., Norman, G., Knox, D., Wood, B. L., Miller, B. D., Emery, C. F., & Berntson, G. G. (2007).
Where to B in dZ/dt. Psychophysiology, 44(1), 113-119. https://doi.org/10.1111/j.1469-8986.2006.00468.x
"""
def __init__(self, moving_average_window: int = 1, handle_missing_events: HANDLE_MISSING_EVENTS = "warn"):
"""Initialize new ``BPointExtractionLozano2007QuadraticRegression`` instance.
Parameters
----------
moving_average_window : int, optional
Window size for moving average filter (in heartbeats, centered around the current heartbeat)
to compute the R-C interval. Default: 1 (no moving average).
handle_missing_events : one 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"``.
"""
super().__init__(handle_missing_events=handle_missing_events)
self.moving_average_window = moving_average_window
[docs] def extract(
self,
*,
icg: IcgRawDataFrame,
heartbeats: HeartbeatSegmentationDataFrame,
c_points: CPointDataFrame,
sampling_rate_hz: float,
):
"""Extract B-points from given ICG derivative signal.
This algorithm extracts B-points using quadratic regression based on the relationship between
the R-C interval and the B-point.
The results are saved in the ``points_`` attribute of the super class.
Parameters
----------
icg : :class:`~pandas.DataFrame`
ICG derivative signal
heartbeats : :class:`~pandas.DataFrame`
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_points : :class:`~pandas.DataFrame`
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_hz : int
sampling rate of ICG derivative signal in hz
Returns
-------
self
Raises
------
:exc:`~biopsykit.utils.exceptions.EventExtractionError`
If the event extraction fails and ``handle_missing`` is set to "raise"
"""
self._check_valid_missing_handling()
is_icg_raw_dataframe(icg)
is_heartbeat_segmentation_dataframe(heartbeats)
is_c_point_dataframe(c_points)
# result dfs
b_points = pd.DataFrame(index=heartbeats.index, columns=["b_point_sample", "nan_reason"])
# used subsequently to store ids of heartbeats where no B was detected because there was no C
# (Bs should always be found, since they are set to the max of the 3rd derivative, and there is always a max)
heartbeats_no_c_b = []
# search B-point for each heartbeat of the given signal
for idx, _data in heartbeats.iterrows():
if self.moving_average_window == 1:
c_point_sample = c_points.loc[[idx], "c_point_sample"]
r_peak_sample = heartbeats.loc[[idx], "r_peak_sample"]
else:
window_width = self.moving_average_window // 2
start_idx = heartbeats.index[max(0, idx - window_width)]
end_idx = heartbeats.index[min(len(heartbeats) - 1, idx + window_width + 1)]
c_point_sample = c_points.loc[start_idx:end_idx, "c_point_sample"].dropna()
r_peak_sample = heartbeats.loc[start_idx:end_idx, "r_peak_sample"].dropna()
# C-point can be NaN, then, extraction of B is not possible, so B is set to NaN
if pd.isna(c_point_sample).any():
heartbeats_no_c_b.append(idx)
b_points.loc[idx, "b_point_sample"] = np.nan
b_points.loc[idx, "nan_reason"] = "c_point_nan"
continue
current_r_peak = heartbeats.loc[idx, "r_peak_sample"]
# get the R-C interval in ms
r_c_interval_ms = np.mean((c_point_sample - r_peak_sample) / sampling_rate_hz * 1000)
if pd.isna(r_c_interval_ms):
b_points.loc[idx, "b_point_sample"] = np.nan
b_points.loc[idx, "nan_reason"] = "no_r_c_interval"
continue
b_point_interval_ms = -0.0032 * r_c_interval_ms**2 + 1.233 * r_c_interval_ms - 31.59
b_point_interval_sample = int((b_point_interval_ms * sampling_rate_hz) / 1000)
b_point_sample = current_r_peak + b_point_interval_sample
b_points.loc[idx, "b_point_sample"] = b_point_sample
b_points = b_points.astype({"b_point_sample": "Int64", "nan_reason": "object"})
is_b_point_dataframe(b_points)
self.points_ = b_points
return self