On anomalies detection in electrocardiograms with unsupervised deep learning methods

Anomaly detection is an important task in various applications and areas of technology and production, such as structural defects, malicious intrusions into management and control systems, financial supervision and risk management, digital health screening, etc. The ever-increasing flows of diverse data and their structural complexity require the development of advanced approaches to their solution. In recent years, deep learning methods have achieved significant success in detecting anomalies, and unsupervised deep learning methods have become especially popular. Methods of anomaly detection by methods of deep learning without a teacher are investigated in the work on the example of a set of electrocardiograms containing normal ECG signals and ECG signals of people with various cardiovascular diseases (anomalies). To detect abnormal electrocardiograms, an autoencoder model has been developed in the form of a deep neural network with several fully connected layers. Also, to solve this problem, a method is proposed for selecting the threshold for separating abnormal ECG signals from normal ones, consisting in optimizing the ratio of performance indicators of the autoencoder model by methods. The paper presents a comparative analysis of the effectiveness of applying various machine learning models, such as the one class Support Vector Method, Isolation Forest, Random Forest and the presented autoencoder model to solving the problem of detecting abnormal ECG signals. For this purpose, metrics such as accuracy, recall, completeness, and f-score were used. His results showed that the proposed model surpassed the other models in solving the problem with accuracy = 98.8% precision = 95.75%, recall = 99.12%, f1-score = 98.75%.