Meta Structural Learning Algorithm With Interpretable Convolutional Neural Networks for Arrhythmia Detection of Multisession ECG

Detection of arrhythmia of electrocardiogram (ECG) signals recorded within several sessions for each person is a challenging issue, which has not been properly investigated in the past. This arrhythmia detection is challenging since a classification model that is constructed and tested using ECG signals maintains generalization when dealing with unseen samples. This article has proposed a new interpretable meta structural learning algorithm for this challenging detection. Therefore, a compound loss function was suggested including the structural feature extraction fault and space label fault with GUMBEL-SOFTMAX distribution in the convolutional neural network (CNN) models. The collaboration between models was carried out to create learning to learn features in models by transferring the knowledge among them when confronted by unseen samples. One of the deficiencies of a meta-learning algorithm is the non-interpretability of its models. Therefore, to create an interpretability feature for CNN models, they are encoded as the evolutionary trees of the genetic programming (GP) algorithms in this article. These trees learn the process of extracting deep structural features in the course of the evolution in the GP algorithm. The experimental results suggested that the proposed detection model enjoys an accuracy of 98% regarding the classification of 7 types of arrhythmia in the samples of the Chapman ECG dataset recorded from 10646 patients in different sessions. Finally, the comparisons demonstrated the competitive performance of the proposed model concerning the other models based on the big deep models.

Automated summary

This article proposes a new interpretable meta structural learning algorithm for the detection of arrhythmia in electrocardiogram signals. By collaborating between models and transferring knowledge, the algorithm maintains generalization when dealing with unseen samples. To improve interpretability, CNN models are encoded as evolutionary trees using genetic programming algorithms. Experimental results show that the proposed model achieves high accuracy and performs competitively compared to other models based on big deep models.