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Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG. | LitMetric

AI Article Synopsis

  • The electrocardiogram (ECG) is crucial for diagnosing heart issues, and new methods like capacitively coupled ECG (ccECG) allow for recordings outside of hospitals.
  • However, this shift can introduce more artefacts in the signals, requiring effective detection algorithms to clean the data.
  • By leveraging transfer learning from existing contact ECG datasets, researchers were able to enhance the artefact detection accuracy in ccECG recordings, with improvements of 5-8% using only 20 segments from those new datasets.

Article Abstract

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833429PMC
http://dx.doi.org/10.3390/s21020662DOI Listing

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