基于双向选择性状态空间模型的心电自动诊断模型  

Automatic ECG diagnosis model based on bidirectional selective state space model

作　　者：林铭俊 温耀棋 张鑫 洪永 陈超敏[1] 吴煜良 LIN Mingjun;WEN Yaoqi;ZHANG Xin;HONG Yong;CHEN Chaomin;WU Yuliang(School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Radiation Oncology Center,Dongguan People's Hospital/the Tenth Affiliated Hospital of Southern Medical University,Dongguan 523059,China)

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]南方医科大学第十附属医院(东莞市人民医院)肿瘤放射治疗中心,广东东莞523059

出　　处：《中国医学物理学杂志》2025年第4期489-495,共7页Chinese Journal of Medical Physics

基　　金：国家重点研发计划(2023YFC2414502)。

摘　　要：针对现有心电自动诊断模型在长时依赖性学习上存在的局限性,提出一种结合双向选择性状态空间模型(BiMamba)与残差多尺度感受野模块的12导联长时心电信号自动诊断模型(BiMamba-RMSF)。首先,设计具有残差连接的多尺度感受野模块实现更广泛的特征提取与融合;其次,引入BiMamba模块通过正向和反向的时序处理方式,提高模型的时序建模能力;最后,分类器对来自BiMamba的特征进行处理实现心电多标签分类任务。从PTB-XL数据集上提取5个主诊断类别的数据,进行五折交叉验证实验。对比实验结果显示,BiMamba-RMSF的平均准确率达到89.42%,平均AUC达到93.56%,平均F1分数达到72.85%,各指标均高于其他4个对比心电自动诊断模型,且通过消融实验进一步验证BiMamba模块的有效性。实验结果表明本文模型在12导联长时心电信号多标签分类任务上具有较高精度。To address the limitations of the existing automatic electrocardiogram(ECG)diagnosis models in learning long-term dependencies,an automatic 12-lead long-term ECG signal diagnosis model which combines bidirectional selective state space model(bidirectional mamba,BiMamba)with residual multi-scale receptive field block(RMSF)is proposed:(1)designing a multi-scale receptive field module with residual connections to realize more extensive feature extraction and fusion;(2)introducing BiMamba block to enhance the model’stemporal modeling capability by employing both forward and backward temporal processing;(3)using the classifier to process features from BiMamba for accomplishing multi-label ECG classification.Five major diagnostic categories from the PTB-XL dataset are extracted and subjected to 5-fold cross-validation experiments.The experimental results from the comparative study show that BiMamba-RMSF achieves an average accuracy of 89.42%,an average AUC of 0.9356,and an average F1 score of 72.85%,outperforming the other 4 automatic ECG diagnosis models.Additionally,ablation study further validates the effectiveness of BiMamba block.It is demonstrated that the proposed model has a high precision in the multi-label classification for 12-lead long-term ECG signals.

关 键 词：心电信号 心电自动诊断模型 选择性状态空间模型 深度学习 

分 类 号：R318[医药卫生—生物医学工程]