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This article investigates electrocardiogram (ECG) acquisition artifacts often occurring in experiments due to human negligence or environmental influences, such as electrode detachment, misuse of electrodes, and unanticipated magnetic field interference, which are not easily noticeable by humans or software during acquisition. 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 to alert experimenters instantly. We put forward a taxonomy of real-time artifacts during ECG acquisition, provide the simulation methods of each category, collect and share a ten-subject data corpus, and investigate machine learning solutions with a proposal of appropriate handcrafted features that reaches an offline recognition rate of 90.89% in a five-best-output person-independent leave-one-out cross-validation. We also preliminarily validate the real-time applicability of our approach.
Liu et al. (Fri,) studied this question.