The article is focused on the development of Siamese neural network models for ECG signals classification that reflect cardiovascular pathologies, including arrhythmias, in the context of a limited amount of training data. The problem of a shortage of training data in machine learning for diagnosing heart disease is associated with a wide variety of pathological states and insufficient information for certain classes in open medical databases. The study aims to develop a complex method based on the combination of high-resolution electrocardiography and vectorcardiography with Siamese neural network architectures and training methods, which makes it possible to improve the accuracy of cardiac arrhythmias classification. The peculiarity of the proposed method, which is based on the ability of Siamese NNs to compare, is to detect and analyse the differences between the ECG signal under study and the generated reference feature vector of signals with pathology, which allows to effectively identify signal changes even for those diseases that are limitedly represented in the training dataset. In addition, to improve the efficiency of training, a method for generating a reference feature vector for pathological signals was developed. This vector is used by the Siamese neural network for comparison. The application of the principal component analysis (PCA) method allowed to extract key features from 100 ECG signals with pathologies, which contributed to the creation of a reference feature vector with a minimum number of training samples. Additionally, for each input ECG signal and reference feature vector, an average cardiac cycle was calculated, which helped to identify low-amplitude ECG components and features of the QRS complex. To implement the developed complex method, the PTB-XL database was used, which contains 12-channel ECG records classified into 70 disease categories. To reduce the impact of data imbalance, augmentation methods, as well as preprocessing methods were used to remove noisy signals and selectively reduce overrepresented classes. Two models of Siamese neural networks (NN) were developed as part of the study. The first model is focused on detecting low-amplitude pathological components of ECG signals, in particular, late atrial and ventricular potentials. The second model is designed to classify 18 types of arrhythmias and 19 associated pathologies, such as coronary heart disease, hypertrophy, and myocardial infarction. The effectiveness of the proposed NN specialised ECGnet network models was evaluated by comparing them with the specialised ECGnet network in the task of recognising late atrial and ventricular potentials. The first model exceeded the accuracy of ECGnet by an average of 10% and reduced the probability of false negative predictions. The second NN model for multi-class classification, which covered 37 diagnostic classes with rare diseases with less than 200 observations, exceeded the average accuracy of ECGnet by 10%, reaching a maximum increase of 28%. The obtained results allow to outline further ways to improve the complex method. In particular, improving the accuracy of ECG signal classification with pathologies is possible by using additional transformations of input features and methods of amplifying low-amplitude signal components.
In the article the problem of identification the shading structure of solar panels is considered. The relevance of the study is conditioned by the necessity of rapid and…
This paper addresses the critical issue of power quality degradation in modern power systems caused by the proliferation of non-linear loads. The study focuses on the…
This study is devoted to the development of a complex method for extracting the fetal electrocardiogram (fECG) from non-invasive abdominal recordings and for the…
You may also start an advanced similarity search for this article.
