Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network

基于深度学习方法对心电图（ECG）数据进行心律失常自动分类检测，对于心律失常的早期筛查具有重要的临床价值，但在有限异常样本监督训练下如何有效地提取心律失常特征是目前亟需解决的难题。本文提出一种基于自适应多特征融合网络的心律失常分类算法，从ECG信号中提取RR间期特征，采用一维卷积神经网络（1D-CNN）提取时域深度特征，并以梅尔频率倒谱系数（MFCC）和二维卷积神经网络（2D-CNN）提取频域深度特征，使用自适应权重策略实现特征融合并进行分类。本文使用麻省理工学院和贝斯以色列医院（MIT-BIH）联合开发的心律失常数据库在患者间范式下评估算法。实验结果表明，所提算法的平均精确率、平均召回率和平均F 1分数分别为75.2%、70.1%和71.3%，分类识别准确率高，可为面向可穿戴设备的心律失常分类提供算法支持。.; Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F 1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.