基于BP算法的MIT-BIH心律失常分类与处理

2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP) Pub Date : 2023-01-06 DOI:10.1109/ISBP57705.2023.10061303

Fumin Mi, Baixuan Li, Xiaojie Cheng, Yunjie Zhao, Minyi Li, Jin Jing

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引用次数: 0

摘要

心电识别对心脏疾病的诊断具有重要意义。基于MIT-BIH心律失常数据库数据，采用小波变换提取更准确的心电信号图，构建BP神经网络进行模式识别，将心律失常分为窦性心律失常、早搏、易波、窦房传导阻滞、房传导阻滞5种类型。并与使用支持向量机和K近邻算法的BP网络进行比较，发现BP网络的性能更好。

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Classification and Processing of MIT-BIH Arrhythmia-Based on BP Algorithm

ECG recognition is of great significance to the diagnosis of heart diseases. Based on the data of the MIT-BIH Arrhythmia Database, a more accurate ECG signal map was extracted using wavelet transform, a BP neural network was constructed for pattern recognition, and five types of arrhythmia-sinus arrhythmia, premature beats, Yibo, sinoatrial block, and atrial block. And compared with the BP network using SVM and K nearest neighbor algorithm, it is found that the BP network performs better.

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2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)

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