NEURAL NETWORK MODULE FOR QUALITY CONTROL OF REGISTERED SIGNALS FOR AMBULATORY PERSONAL TELEMONITORING ECG SYSTEMS

Abstract

Background. The article is devoted to the development of an ambulatory personal telemonitoring ECG systems (APTECG systems). The relationship of increasing the reliability of decisions made in APTECG systems with quality control of recorded ECGs is shown. The expediency of including the quality control module of registered ECGs in the APTECG systems is proved. Materials and methods. A block diagram of the APTECG system with a quality control module of registered ECGs in its composition has been developed. Its functions and connections with other modules are defined. The quality control module of registered ECGs is implemented as a neural network binary classifier based on 2D CNN. To represent the ECGs as an image, a wavelet transform was used. A database of images has been created for training and testing neural networks. Results and conclusions. Four deep learning neural networks have been developed in the Python programming language, according to the test results of which the VGGNet19 network showed the best result with an accuracy of 0.97, a logarithmic loss of 0.1 and an F-measure of 0.97. The developed block diagram of the APTECG system and the quality control module of the registered ones will increase the reliability of the diagnostic decisions made.

Keywords

• ecgs quality
• artificial neural networks
• deep learning
• ambulatory personal telemonitoring ecg systems