Development of Neural Network-Based Approach for QRS Segmentation

Abstract

The paper is devoted to the development of QRS segmentation system based on deep learning approach. The considered segmentation problem plays an important role in the automatic analysis of heart rhythms, which makes it possible to identify life-threatening pathologies. The main goal of the research is to choose the best segmentation pipeline in terms of accuracy and time-efficiency. Process of ECG-signal analysis is descripted and the problem of QRS segmentation is discussed. State-of-the-art algorithms are analyzed in literature review section and the most prominent are chosen for further research. In the course of the research three hypotheses about appropriate deep learning model are checked: LSTM-based model, 2-input 1- dimensional CNN model, and “signal-to-picture” approach based on 2-dimensional CNN. All the architectures are tested, and their advantages and disadvantages are discussed. The proposed ECG segmentation pipeline is developed for Holter monitor software.

Keywords

• electrocardiogram (ecg)
• holter ecg analysis system
• ecg segmentation
• qrs complex detection
• deep learning
• convolutional neural network (cnn)
• long short-term memory (lstm)