Autoencoder Neural Networks for Outlier Correction in ECG- Based Biometric Identification

@article{Karpinski2018AutoencoderNN,
  title={Autoencoder Neural Networks for Outlier Correction in ECG- Based Biometric Identification},
  author={M. Karpinski and V. Khoma and Valerii Dudvkevych and Y. Khoma and D. Sabodashko},
  journal={2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)},
  year={2018},
  pages={210-215}
}
  • M. Karpinski, V. Khoma, +2 authors D. Sabodashko
  • Published 2018
  • Computer Science
  • 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)
The paper presents a novel method based on autoencoder neural networks for detection and correction of ECG heartbeats outliers. [...] Key Method, optimal autoencoder architecture for the detecting of electrocardiogram (ECG) outliers was chosen. In order to validate our method., we used the open source Physionet ECG-ID database. Results obtained in the paper have been compared to previously developed techniques. On the one hand autoencoder demonstrates slightly higher error rate., but is much easier to…Expand
2 Citations

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