Research On Multi-Modality Deep Learning Model For Arrhythmia Classification

Arrhythmia is a common cardiovascular disease with a high fatality rate and wide incidence population,and its vast harm has attracted the widespread attention of researchers.After entering the era of deep learning,arrhythmia analysis has been greatly improved in diagnostic efficiency and classification accuracy.Multi-modality deep learning can fully represent the data and improve model performance.Therefore,based on multi-modality features and deep learning methods,this paper proposes three multi-modality classification models with different ideas for the purpose of improving the performance of arrhythmia classification.Among them,in order to alleviate the problem of a single feature,the channel attention-based multi-scale convolution multi-modality arrhythmia classification model is proposed.To further alleviate the data imbalance problem,the data augmentation algorithm for multi-modality data interaction is designed,and the data augmentation-based dual-encoded multi-modality arrhythmia classification model is proposed.Finally,to further explore the inter-modality correlation,the multi-attention mechanism-based multi-modality ventricular arrhythmia classification model is proposed to explore more practical application value.The specific research and innovations are summarized as follows:(1)We proposed a channel attention-based multi-scale convolution multi-modality arrhythmia classification model.The performance of the model is improved by extracting features of different sizes.The model consists of three parts: multi-scale convolution module and channel attention multi-modality features fused module.In the multi-scale convolution module,two convolution kernels with different scales are used to extract the signal and image modalities features under different sizes;In the channel attention multi-modality features fused module,further attention is paid to the inter-channel information of the fusion features extracted by the same size convolution kernel.Then,the two feature vectors obtained by the channel attention network are concatenated,and the convolutional layer can obtain the fusion feature.The classification result is output through the classifier.The model is evaluated on the MIT-BIH Arrhythmia Database and compared with other methods.The results show that the channel attention-based multi-scale convolution multi-modality arrhythmia classification model can accurately classify arrhythmias,with an average accuracy of98.06% and an average F1＿score of 87.24%.(2)We proposed a data augmentation-based dual-encoded multi-modality arrhythmia classification model.Designing the data augmentation algorithm for multi-modality data interaction to alleviate the problem of data imbalance,and the model performance is improved.The model consists of a multi-modality data augmentation module and a dual-encoded multi-modality feature extraction module.The multi-modality data augmentation module generates new samples and expands minority class samples to obtain a balanced training dataset by interacting with multi-modality data;The dual-encoded multi-modality feature extraction module extracts feature information from two modalities of ECG signal and image through the one-dimensional and two-dimensional encoder structures,respectively,and uses them for model classification after feature fusing.The model is evaluated on the MIT-BIH Arrhythmia Database and compared with other methods.The results show that the data augmentation-based dual-encoded multi-modality arrhythmia classification model can alleviate the class imbalance problem and improve the classification performance,achieving an average accuracy of 98.83% and an average F1＿score of 91.96%.(3)We proposed a multi-attention mechanism-based multi-modality ventricular arrhythmia classification model,and the multi-modality features are fully fused through the multi-attention mechanism to improve the accuracy of the model.The model consists of a multi-modality feature extraction encoding stage and a multi-attention feature fusion stage.In the first stage,the data of the two modalities are sent to the corresponding feature encoders,respectively,and two single-modality features are extracted through the residual network;In the second stage,the two single-modality features are fused through the cross-attention network,and the self-attention network is used to obtain the fusion features.The model is evaluated on the MIT-BIH Arrhythmia Database and compared with other methods based on the Inter-patient paradigm.The results show that the multi-attention mechanism-based multi-modality ventricular arrhythmia classification model can obtain high classification accuracy,especially in ventricular arrhythmia.The model has an average accuracy of 97.72%,a sensitivity of 83.29%,and a precision of 81.87% on ventricular arrhythmias.