@Article{JICS-17-075,
author = {Zhangxiao Miao and Chunzheng Cao},
title = {Classification of ECG Signals Based on Functional Data Analysis},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {17},
number = {1},
pages = {075--080},
abstract = {<p style="text-align: justify;">Electrocardiogram (ECG) signals are the impulses generated by the heart which are used to
analyze the proper functioning of heart. This paper applies a functional data analysis method to classify ECG
signals. The classification of functional data can be divided into regression model-based classification methods
and density-based classification methods. In this paper, Generalized Functional Linear Models (GFLM) and
Functional Linear Discriminant Analysis (FLDA) are introduced. Finally, we apply GFLM, FLDA and SVM,
neural network, KNN in the actual data, and find that the functional data classification method performs better
in the classification of ECG signals.</p>},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22364.html}
}