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ECG to SQL

ecg_to_sql.png

ECG to SQL is a collection of python scripts to store electrocardiography (ECG) data into an SQLite database. The data used to develop this repo are provided by the PhysioNet/CinC challenge 2021 on the detection of atrial fibrillation.

The database structure is mainly based on the online analytical processing (OLAP) approach with the following entity relationship diagram. eer_diagram

To ensure data integrity during entries insertion and minimize data redundancy, the design of this dataset satisfies the first three normal form rules. The ecg time series were stored into a single table per patient.

Usage

Aggregate all the metadata in csv files, one for each dataset

from data_access.prepare import prepare_summary_csv

prepare_summary_csv()

Create and populate the dataset

from create_db import CreateDb
from pathlib import Path
from parameters import DefaultArguments

data_dir = Path('./path')
builder = CreateDb(data_dir, db_file_name='af_detection.db')

builder.populate_schema(dataset_name=DefaultArguments.ChapmanShaoxing)
builder.populate_data_tables(dataset_name=DefaultArguments.ChapmanShaoxing)
builder.db.close()

Load the dataset and retrieve one patient data

from load_db import LoadDb
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np

data_dir = Path('./path')
loader = LoadDb(data_dir=data_dir, 
                db_file_name='af.db')

original_id, diagnosis = loader.get_single_patient_data(patient_id=42)
ecg = loader.get_ecg(patient_id=42, leads=[1])

time = np.array(list(range(len(ecg)))) / loader.SAMPLING_FREQUENCY
plt.title(f'Patient id: {original_id} | diagnosis: {diagnosis}')
plt.xlabel('Time (s)')
plt.ylabel('mV')
plt.plot(time, ecg)

test_JS05301.png

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Gaetano Scebba - @GScebba

Acknowledgments