Abstract: 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 proposedalgorithm shows good performance in the multi-classification of ECG signals.

Key words: ECG signal classification, Multi-task learning, Loss optimization