Atrial fibrillation detection based on ECG

Mingxin Dong; Yuan Gao; Jiang Yue; Yushen Liu; Mengxin Xie

doi:10.1117/12.2640954

3 October 2022 Atrial fibrillation detection based on ECG

Mingxin Dong, Yuan Gao, Jiang Yue, Yushen Liu, Mengxin Xie

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900O (2022) https://doi.org/10.1117/12.2640954
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

Abstract

As a common illness, atrial fibrillation is difficult to detect, so accurate detection is necessary to ensure better hospital treatment. In this work, we study three topics: the performance of two standard classifiers called Support Vector Machine and Logistic Regression, the impact on the classification result caused by imbalanced data, and which features influence the training model. In evaluating the performance of classifiers, we use four parameters: precision, recall, F1-score, and accuracy. Besides, we also use the ROC curve and the confusion metrics to support our findings. Finally, we get a good classification result and prove that the feature consists of a robust coefficient of variation, and the Skewness parameter shows the most impact on the training model.

Citation Download Citation

Mingxin Dong, Yuan Gao, Jiang Yue, Yushen Liu, and Mengxin Xie "Atrial fibrillation detection based on ECG", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900O (3 October 2022); https://doi.org/10.1117/12.2640954

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