title: ÐÐ·Ð²Ð»ÐµÑÐµÐ½Ð¸Ðµ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð­ÐÐ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Extract diagnostic information from ECG images using deep learning techniques
Creators: name: ÐÐ¾ÑÑÐ¸Ð½, ÐÐµÐ¾ÑÐ³Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
ÑÐ»ÐµÐºÑÑÐ¾ÐºÐ°ÑÐ´Ð¸Ð¾Ð³ÑÐ°Ð¼Ð¼Ð°
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
u-net
yolact
Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ðµ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸
computer vision
electrocardiogram
convolutional neural netÂ¬works
segmentation
deep learning
extracting information

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð­ÐÐ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ»Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸. ÐÐ½Ð°Ð»Ð¸Ð· Ð¿Ð¾ÐºÐ°Ð·Ð°Ð» Ð¾Ð± Ð½ÐµÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¸Ð·-Ð·Ð° Ð¸Ð¼ÐµÑÑÐ¸ÑÑÑ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¾Ð², ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº Ð½ÐµÐ²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ ÑÐ²ÑÐ·Ð½Ð¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð·Ð¾Ð½Ñ Ð­ÐÐ-ÑÐ¸Ð³Ð½Ð°Ð»Ð°, Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¿Ð¾ÑÑÐµÐ´ÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ°ÑÑÐ¸Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ Ð² ÑÐ»ÑÑÐ°ÑÑ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ° Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐ¸Ð²Ð°ÑÑÐ¸Ð¼ Ð¿ÑÑÐ¼Ð¾ÑÐ³Ð¾Ð»ÑÐ½Ð¸ÐºÐ¾Ð², Ð²Ð²Ð¾Ð´Ð° ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° ÐºÐ»Ð°ÑÑÐµÑÐ¾Ð² Ð¸ Ð´ÑÑÐ³Ð¸Ñ Ð°ÑÐ¿ÐµÐºÑÐ¾Ð². ÐÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° U-Net Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð´Ð»Ñ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð¸ Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐ¸Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð­ÐÐ-ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸ Ð­ÐÐ, Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð»Ð¾ÑÑ Ð½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¼ Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ. Ð ÑÑÐµÐ´Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ MATLAB Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿Ð¾ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¼Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð¾ÑÐ¸ÑÑÐ¾Ð²ÐºÐ¸ Ð­ÐÐ-ÑÐ¸Ð³Ð½Ð°Ð»Ð°. ÐÑÐ»Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½ÑÐ° Dice Ð´Ð»Ñ Ð¾ÑÐµÐ½Ð¸Ð²Ð°Ð½Ð¸Ñ Ð´Ð¾ÑÑÐ¾Ð²ÐµÑÐ½Ð¾ÑÑÐ¸ ÑÐ°Ð±Ð¾ÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ, Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ ÐºÐ¾ÑÐ¾ÑÑÑ ÑÐ²Ð¸Ð´ÐµÑÐµÐ»ÑÑÑÐ²ÑÑÑ Ð¾ Ð²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸.
description: This work is devoted to the implementation of the algorithm for extracting diagÂ¬nostic information from the ECG using deep learning methods. Methods based on deep learning and classical segmentation algorithms were considered and analyzed. The analysis showed the inefficiency of using classical methods to solve the problem due to the existing shortcomings, such as the inability to provide a coherent segmented zone of the ECG signal, the need for direct user participation in cases of object allocation by bounding rectangles, entering the number of clusters, and other aspects. The U-Net neural network architecture was chosen to find and limit the ECG signal region in the ECG image, which was trained on the developed data set. In the development environment of the MATLAB package, an algorithm for automatic segmentation and digitization of the ECG signal was implemented. EstiÂ¬mates of the bone coefficient were obtained for evaluating the reliability of the neural network architecture, the values of which indicate the high accuracy of the proposed method for solving the problem