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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.
}, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22364.html} }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.