Research on Atrial Fibrillation Data by Principal Component Analysis

DOI

10.2991/snce-18.2018.52How to use a DOI?

Keywords

Atrial fibrillation; Principal component analysis; Data correction

Abstract

In the case of Gauss distribution, the principal component analysis is equivalent to the maximum amount of information in the output signal. The theory and practice of principal component analysis (PCA) is concise, and the results coincide with the objective reality, making it widely applied in the fields of economy, society and engineering. First, we discuss the characteristics of the room electric signal, clarify the significance of the estimation of the main frequency. Secondly, we study the mathematical modeling method of the main frequency estimation, improve the Furiour Algorithm and the harmonic decomposition method, and propose a multiple signal classification algorithm. Finally, we compare the characteristics of different algorithms, and estimate the accuracy of the main frequency, and select the appropriate main frequency estimation algorithm to help the electrocardiologists determine the target ablation risk frequency and improve the efficiency of ablation operation.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Tang
PY  - 2018/05
DA  - 2018/05
TI  - Research on Atrial Fibrillation Data by Principal Component Analysis
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 261
EP  - 264
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.52
DO  - 10.2991/snce-18.2018.52
ID  - Tang2018/05
ER  -