## 1 Introduction
### Related Work  
The application of deep learning techniques in processing different physiological signals was summarized by [PERSON] et al. They analyzed 53 publications from 1 January 2008 to 31 December 2017, 17 concerning ECGs [3]. Another review of using DL methods for ECG data was performed by [PERSON] et al. in [4]. They evaluated 191 articles published between 1 January 2010 and 29 February 2020. The authors of this work confirm that the application of DL methods in ECG analysis (including signal annotation) is becoming an increasingly frequently discussed topic.  
The most frequently discussed issue in relation to the ECG signal is undoubtedly the anomaly detection. [PERSON] et al. used DL methods for premature ventricular contractions (PVCs) localization. On the CPSC2018 (China Physiological Signal Challenge 2018) database, they achieved the dice coefficient 0.947 for the model with a max pooling layer [5].  
[PERSON] et al. presented a fine-grained multilabel ECG framework to detect correlated cardiac abnormalities in clinical ECG data using convolutional neural network (CNN) and recurrent neural network (RNN) [6]. The authors of [7] used U-Net with bidirectional LSTM for the automated detection of QRS complexes. They achieved an accuracy of 78.73% and 98.29% on the CPSC2019 (China Physiological Signal Challenge 2019) and mitdb datasets, respectively.  
In their work, [PERSON] and [PERSON] used transfer learning for improving CNNs classification of heart rhythm and atrial fibrillation (AF) from short ECG signals [8]. The authors explored both unsupervised and supervised pretraining of CNN on the Icentia11K (Icentia11K contains ECG data from 11,000 patients, who wore a monitoring device for up to two weeks resulting in 630,000 h of ECG signal with over 2,700,000,000 beats labeled by the device and then again by a specialist) dataset. They showed that pretraining improves CNN's performance by over 6% and scored the maximum of F\({}_{1}\)-score 0.926 for beat classification.  
Another interesting issue is the analysis of the ECG signal in terms of detecting individual features in physiological biometrics. [PERSON] et al., in their work, used BP and DNN for ECG-based identification [9]. They created and tested their model on two databases: mitdb (MIT arrhythmia database) and a self-collected one. The database consisted of signals obtained during various emotions. The authors achieved a 94.39% recognition rate on the combined datasets.  
An automatic segmentation of a signal, which is nothing more than finding the reference points within it, is crucial in order to perform automatic interpretation of the signal. There are many different algorithms for segmentation of the ECG signal that use many signal processing methods and work on different databases. In their work, [PERSON] and [PERSON] compared algorithms for ECG segmentation [10]. The authors performed signal segmentation on ECG using nine different algorithms [11; 12; 13; 14; 15; 16; 17; 18; 19] and PhysioNet's  
Figure 1: A sample 1D CNN with three-convolutional and two-linear layers [2].  
QT database [20]. They achieved the best results while using probabilistic methods and methods based on wavelet transform.  
In our study, we propose an alternative approach for the segmentation of a one-dimensional signal segmentation such as ECG. We created a tool for semantic segmentation that used a one-dimensional U-Net containing squeeze-excitation blocks.