Detail publikace

Adaptive recurrent system for noise cancellation and arrhythmia detection

Originální název

Adaptive recurrent system for noise cancellation and arrhythmia detection

Anglický název

Adaptive recurrent system for noise cancellation and arrhythmia detection

Jazyk

en

Originální abstrakt

A new LMS-based adaptive filter for preprocessing and analysis of ECG signals recorded under conditions of stress-tests is introduced. The adaptive filter was developed to cancel motion artifacts and myopotentials and to detect diagnostically significant waves in the ECG signal, e.g. arrhythmias. The new filter exploits a repetitivity of the ECG signal for optimal removing of the nonstationary noise. It uses a vector-variable-step-size LMS algorithm to adjust filter weights according to the nonstationary properties of the processed signal. The developed system was tested on real signals and on the CSE signal library, and the results are described.

Anglický abstrakt

A new LMS-based adaptive filter for preprocessing and analysis of ECG signals recorded under conditions of stress-tests is introduced. The adaptive filter was developed to cancel motion artifacts and myopotentials and to detect diagnostically significant waves in the ECG signal, e.g. arrhythmias. The new filter exploits a repetitivity of the ECG signal for optimal removing of the nonstationary noise. It uses a vector-variable-step-size LMS algorithm to adjust filter weights according to the nonstationary properties of the processed signal. The developed system was tested on real signals and on the CSE signal library, and the results are described.

BibTex


@inproceedings{BUT11303,
  author="Ivo {Provazník} and Jiří {Holčík}",
  title="Adaptive recurrent system for noise cancellation and arrhythmia detection",
  annote="A new LMS-based adaptive filter for preprocessing and analysis of ECG signals recorded under conditions of stress-tests is introduced. The adaptive filter was developed to cancel motion artifacts and myopotentials and to detect diagnostically significant waves in the ECG signal, e.g. arrhythmias. The new filter exploits a repetitivity of the ECG signal for optimal removing of the nonstationary noise. It uses a vector-variable-step-size LMS algorithm to adjust filter weights according to the nonstationary properties of the processed signal. The developed system was tested on real signals and on the CSE signal library, and the results are described.",
  address="IEEE",
  booktitle="Proceedings of the 16th Annual International Conference of the IEEE EMBS",
  chapter="11303",
  institution="IEEE",
  year="1994",
  month="october",
  pages="1270--1271",
  publisher="IEEE",
  type="conference paper"
}