ABPImputation

ABPImputation is a package for imputing the arterial blood pressure (ABP) waveform using non-invasive physiological waveforms (photoplethysmogram, or PPG, and electrocardiogram, or ECG/EKG). For detailed information, please see our paper: Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

Installation

From Github repo

Clone the repo and install the required packages:

git clone https://github.com/brianhill11/ABPImputation.git
cd ABPImputation
pip install -r requirements.txt

Data

Data download

The preprocessed MIMIC .npy files can be downloaded here.

Files containing train/validation/test patient IDs can be found in the abpimputation/data/metadata directory.

Data format

We assume that the input waveform data has been sampled at 100 Hz.

The raw data should be in the following format:

,ekg,ppg,art,pseudo_NIBP_sys_5min,pseudo_NIBP_dias_5min,pseudo_NIBP_mean_5min
2192-01-15 18:05:13.000,0.2942022306679429,0.00014857817077067377,0.01588212121779295,,,
2192-01-15 18:05:13.010,0.3923739047014039,-0.000148964018089602,-0.0159184022120733,,,
2192-01-15 18:05:13.020,0.36888112077855467,0.00014878046741923878,0.015916127098078936,,,
2192-01-15 18:05:13.030,0.39216988733528724,-0.0001497382374610061,-0.0159912403262073,,,
2192-01-15 18:05:13.040,0.36341014241462266,0.00014898555592997471,0.015950437232792714,,,
2192-01-15 18:05:13.050,0.3878550380346896,-0.00014980310141308973,-0.016016143459373855,,,

The first column is the timestamp index, which is assumed to be in sorted order. Column names for each signal must either be 'ekg' for electrocardiogram, 'ppg' for photoplethysmogram, and 'art' (if available) for arteral line blood pressure, or they should be mapped to such using the signal_column_names dict in the project_configs.py file.

Non-invasive blood pressure (NIBP) columns should be specified using the nibp_column_names variable in the project_configs.py file, using the order: systolic, diastolic, mean.

Preprocessing

Generating predictions with pre-trained model

Generating predictions using the pre-trained model generally requires 3 steps:

1. loading waveform data (in the expected format, see Data format section)
2. preprocessing the waveform data to add additional features
3. load the pre-trained model and generated the imputed waveform(s)

Here is an example code segment:

from abpimputation.ABPImputer import ABPImputer
from abpimputation.preprocessing.preprocess import preprocess

# read in the data from file
data = pd.read_csv(f, index_col=0)

# preprocess the data 
preprocessed_data = preprocess(data)

# split into input/target data
y_true = preprocessed_data["art"]
X = preprocessed_data.iloc[:, :-1]
# add in pseudo-time axis 
X = np.expand_dims(X, axis=1)

# instantiate ABPImputer
abp = ABPImputer()

# generate predicted ABP waveform
y_pred = abp.predict(X)

Calibrating the model using additional data

Training from scratch

Citation

If you use the ABPImputation package in your work, please cite

Hill, B.L., Rakocz, N., Rudas, Á. et al. Imputation of the continuous arterial line 
blood pressure waveform from non-invasive measurements using deep learning. 
Sci Rep 11, 15755 (2021). https://doi.org/10.1038/s41598-021-94913-y