Omkar2118/datasets huggingface dataset:
QT-NSTDB
Full name: QT database + MIT-BIH Noise Stress Test Database (NSTDB)

We designed a baseline wander (BLW) removal benchmark to evaluate various methods using a consistent test set and uniform conditions. Details of the data preprocessing pipeline are heavily based on papers [1].
All 105 signals from the QT Database were resampled from 250 Hz to 360 Hz to align with the NSTDB sampling frequency. Heartbeats were extracted using the annotations provided by specialists. During this process, we identified a small number of incorrect annotations for beat start/end points, leading to cases where two consecutive beats were erroneously merged into one. To address this issue, we discarded beats exceeding 512 samples (1422.22 ms) in length.
We designated heartbeats from 14 signals, accounting for 13% of the total signals, as the test set. These signals were selected to include two signals from each of the seven datasets comprising the QT Database, ensuring a diverse representation of pathologies in the test set. This setup provides a more robust evaluation of the generalization capability of the methods under consideration.
Noise from the NSTDB, specifically BLW caused by breathing and electrode movement (referred to as the "em" signal in the database), was used to contaminate the ECG signals. The "em" record was split to match the length of the beat samples, with Channels 1 and 2 concatenated. Additionally, 13% of each channel’s signal length was preserved to contaminate the signals assigned to the test set.
This separation of the test signals ensures the reliability of results when employing learning algorithms, preventing any unfair advantage over classical methods. For noise injection, we adopted the same approach as the NSTDB, randomly injecting noise with amplitudes ranging from 0.2 to 2 times the ECG signal’s maximum peak value. The mixed dataset can be downloaded from here: https://drive.google.com/file/d/19qOwywAoxreEv4xONTk-smQdo-ZdoPBc/view.

[1] Romero, F. P., Piñol, D. C., & Vázquez-Seisdedos, C. R. (2021). DeepFilter: An ECG baseline wander removal filter using deep learning techniques. Biomedical Signal Processing and Control, 70, 102992.

Introducing paper: DeepFilter: an ECG baseline wander removal filter using deep learning techniques