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ECG library with pretrained models and tools for ECG analysis

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Ecg library (ecglib) is a tool for ECG signal analysis. The library helps with preprocessing ECG signals, downloading the datasets, creating Dataset classes to train models to detect ECG pathologies and EcgRecord classes to store their records. The library allows researchers to use model architectures pretrained on more than 500,000 ECG records to fine-tune them on their own datasets.


If you find this tool useful in your research, please consider citing the paper:

  1. Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification - We demonstrate that training deep neural networks on a large dataset and fine-tuning it on a small dataset from another domain outperforms the networks trained only on one of the datasets.

         title={Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification},
         author={Avetisyan, Aram and Tigranyan, Shahane and Asatryan, Ariana and Mashkova, Olga and Skorik, Sergey and Ananev, Vladislav and Markin, Yury},
         journal={arXiv preprint arXiv:2305.18592},


To install the latest version from PyPI:

pip install ecglib


This module allows user to load and store ECG datasets and records in different formats and to extract meta-information about each single ECG signal (i.e. frequency, full path to file, scp-codes, patient age etc.).

Via one can download PTB-XL ECG database in its original wfdb format and to store information concerning each record in a csv file.

# download PTB-XL ECG dataset

from import load_ptb_xl

ptb_xl_info = load_ptb_xl(download=True)

Via one can create class EcgRecord to store important information about an ECG record.

# creating EcgRecord class for example file

from import EcgRecord
import wfdb

ecg_signal = wfdb.rdsamp("wfdb_file")[0] # for example 00001_hr from PTB-XL dataset
ecg_record = EcgRecord(signal=ecg_signal.T, frequency=500, patient_id=1)

Via one can create class EcgDataset to store ECG datasets. It stores main features of your ECG dataset such as number of leads, number of classes to predict, augmentation etc. It is also possible to plot each record using ecg-plot.

# create EcgDataset class from 
# fit targets for 'AFIB' binary classification

from import EcgDataset 

targets = [[0.0] if 'AFIB' in eval(ptb_xl_info.iloc[i]['scp_codes']).keys() else [1.0] 
           for i in range(ptb_xl_info.shape[0])]
ecg_data = EcgDataset(ecg_data=ptb_xl_info, target=targets)


This module comprises components of model architectures and open weights for models derived from binary classification experiments in several pathologies.

create_model function allows user to create a model from scratch (supported architectures include resnet1d18, resnet1d50, resnet1d101, densenet1d121, densenet1d201) as well as load a pretrained model checkpoint from weights folder (supported architectures include resnet1d18, resnet1d50, resnet1d101, densenet1d121). create_model also allows to use both ECG record and metadata during training by concating FCN to the network that takes ECG record as an input.

# create 'densenet1d121' model from scratch for binary classification 12-lead experiment

from ecglib.models.model_builder import create_model

model = create_model(model_name='densenet1d121', pathology='1AVB', pretrained=False)

# create 'cnntabular' model with 'densenet1d121' architecture for ECG record and FCN for metadata. Number of input features is set to 5 by default and can be changed by adding config

from ecglib.models.model_builder import Combination
from ecglib.models.config.model_configs import DenseNetConfig, TabularNetConfig

densenet_config = DenseNetConfig()
tabular_config = TabularNetConfig(inp_features=50)
model = create_model(model_name=['densenet1d121', 'tabular'],
                     config=[densenet_config, tabular_config],
# load pretrained 'densenet1d121' model from 'weights' folder for binary classification 12-lead experiment

from ecglib.models import create_model

model = create_model(model_name='densenet1d121', pathology='AFIB', pretrained=True)

architectures folder includes model architectures.

In weights folder one can find file with paths to the models derived from the following binary classification 12-lead experiments. Available pathologies (scp-codes): AFIB, 1AVB, STACH, SBRAD, IRBBB, CRBBB, PVC.


This module includes framework inspired by Albumentations Python library for preprocessing and augmenting ECG data. script contains SomeOf, OneOf and Compose structures for building your own preprocessing and augmentation pipeline. and both comprise classes and functions respectively describing different preprocessing and augmentation techniques. You can preprocess either numpy data and EcgRecord data. For more information see code commentary.

# augmentation example
import torch
from ecglib.preprocessing.preprocess import *
from ecglib.preprocessing.composition import *

# provide an ecg record in a `numpy.ndarray` form
ecg_signal = wfdb.rdsamp("wfdb_file")[0] # for example 00001_hr from PTB-XL dataset
ecg_record = EcgRecord(signal=ecg_signal.T, frequency=500, patient_id=1)

augmented_record = Compose(transforms=[
    SumAug(leads=[0, 6, 11]), 
    OneOf(transforms=[ButterworthFilter(), IIRNotchFilter()], transform_prob=[0.8, 0.2])
], p=0.5)(ecg_record) # ecg_signal can be used instead of ecg_record


This module allows users to test trained model with the architecture from ecglib. You can get the prediction for the specific ECG record or the prediction for all the records in the directory.

# Predict example
from ecglib.predict import Predict

ecg_signal = wfdb.rdsamp("wfdb_file")[0] # for example 00001_hr from PTB-XL dataset

predict = Predict(

result_df = predict.predict_directory(directory="path/to/data_to_predict",
print(predict.predict(ecg_signal, channels_first=False))

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