基于梯度幅值方向调整的心电信号多任务分类算法

Multitask Classification Algorithm of ECG Signals Based on Radient Magnitude Direction Adjustment

心血管疾病对人类生命健康安全的威胁日益严重,通过心电信号可进行相关疾病的诊断分类。现有的心电分类算法大多采用单任务学习模型,无法综合利用多个任务中的互补特征,而多任务学习模型可同时学习多个相关任务,共享相关任务特征,有助于提高多任务的分类表现。结合深度学习和多任务学习两种方法,提出了一种基于损失优化的心电信号多任务分类算法,将心电信号的多分类任务分解为多个二分类任务,从任务梯度的幅值和方向两方面进行损失优化,避免手动设置任务损失权重以及任务损失相互抵消而产生的负迁移,从而提升心电信号多分类任务的性能。在PTB-XL数据库上将心电信号23类分类任务分解为23个二分类任务来评估所提出的算法。实验结果表明,所提算法的宏观曲线下平均面积(AUC)达到0.950,准确率达到96.50%,基于标签的宏观F1分数达到0.583,基于样本的F1分数达到0.777。与单任务学习算法相比,所提算法在心电信号的多分类方面表现出良好的性能。

Cardiovascular diseases are posing more and more serious threats to human health and safety.ECG signals can be used to diagnose and classify related diseases.Most existing ECG classification algorithms adopt single-task learning model,which can not make comprehensive use of complementary features in multiple tasks.However,multi-task learning model can learn multiple related tasks at the same time,share related task features,and help improve the classification performance of multiple tasks.Combining deep learning and multi-task learning,a multi-task classification algorithm for ECG signals based on loss optimization is proposed.The multi-classification task of ECG signals is decomposed into multiple binary classification tasks,and loss optimization is carried out from the aspects of the amplitude and direction of task gradient,so as to avoid the negative transfer caused by manual setting of task loss weights and the cancellation of task losses.The performance of ECG signal multi-classification task is improved.The model uses PTB-XL database to decompose 23 classification tasks into 23 binary classification tasks to evaluate the proposed algorithm.Experimental results show that the average area under the macro curve(AUC)reaches 0.950,the accuracy reaches 96.50%,the tag-based F1 score reaches 0.583,and the sample-based F1 score reaches 0.777.Compared with the single-task learning algorithm,the proposed algorithm shows good performance in the multi-classification of ECG signals.