sensORder: Artifact Classification during Biosignal Acquisition


The quality of the obtained biosignals largely influences the various subsequent research and experiments. 

This research investigates biosignal acquisition artifacts frequently occurring in experiments due to human negligence or environmental influences, such as electrode detachment, misuse of electrodes, unanticipated magnetic field interference, and signal distortion by human movements, which are not easily noticeable by experimenters or software during acquisition but can be discovered by ML in real-time. Such artifacts usually result in useless and irreparable signals; therefore, it would be a great help to research if the problems are detected during the acquisition process, and the experimenters are alerted to them.


A starter Dataset of ECG:

ECG, a very common biosignal, is used as the initiating study object of the artifacts during biosignal acquisition. The overall research framework of taxonomy and real-time classification of ECG acquisition artifacts can provide a superior reference value for researching other bioelectrical signals, like EXGs. It can also radiate to more biosignal types, such as inertial biosignals from accelerometers, gyroscopes, or magnetometers.

The dataset sensORder-ECG-2023 can be downloaded here.


Privacy Preservation and Data Security:

The participant's consent form stipulates that the use of the data is limited to non-commercial research purposes, and the data users guarantee not to attempt to identify the participating persons. Furthermore, the data users guarantee to pass on the data (or data derived from it) only to third parties who are bound by the same rules of use (for non-commercial research purposes, no identification attempts, restricted disclosure). Data users who violate the usage regulation mentioned above will bear the legal consequences themselves, where the dataset publisher takes no responsibility.


Citation Request:

This dataset is freely available for non-commercial academic research. We would appreciate referencing the below publications if you use this dataset or the implementation approaches related to it:

Taxonomy and Real-Time Classification of Artifacts during Biosignal Acquisition: A Starter Study and Dataset of ECG, (Hui Liu, Shiyao Zhang, Hugo Gamboa, Tingting Xue, Congcong Zhou, Tanja Schultz), In IEEE Sensors Journal, 2024.