Semantic Segmentation of 12-Lead ECG Using 1D Residual U-Net with Squeeze-Excitation Blocks

Analyzing biomedical data is a complex task that requires specialized knowledge. The development of knowledge and technology in the field of deep machine learning creates an opportunity to try and transfer human knowledge to the computer. In turn, this fact influences the development of systems for the automatic evaluation of the patient's health based on data acquired from sensors. Electrocardiography (ECG) is a technique that enables visualizing the electrical activity of the heart in a noninvasive way, using electrodes placed on the surface of the skin. This signal carries a lot of information about the condition of heart muscle. The aim of this work is to create a system for semantic segmentation of the ECG signal. For this purpose, we used a database from Lobachevsky University available on Physionet, containing 200, 10-second, and 12-lead ECG signals with annotations, and applied one-dimensional U-Net with the addition of squeeze-excitation blocks. The created model achieved a set of parameters indicating high performance (for the test set: accuracy-0.95, AUC-0.99, specificity-0.95, sensitivity-0.99) in extracting characteristic parts of ECG signal such as P and T-waves and QRS complex, regardless of the lead.

Automated summary

Analyzing biomedical data requires specialized knowledge. The development of deep machine learning creates an opportunity to transfer human knowledge to computers, influencing the development of automatic patient health evaluation systems based on sensor data. This study aims to create a system for semantic segmentation of ECG signals using a one-dimensional U-Net model with squeeze-excitation blocks, achieving high performance in extracting characteristic parts of the ECG signal.