ECG Segmentation by Neural Networks: Errors and Correction

The effect of error correction oftenappears in ensembles of neural networks: it is known that, in most cases, an ensemble can improve the effectiveness of the base network [2]. The creation of an ensemble of models is widely used in modern machine learning as the last step of the working pipeline. However, it is difficult to predict which mistakes the ensemble can eliminate from the basic model and which can not. This problem of possible mistakes of the trained model remains relevant because the representation of the data learned by the neural network is difficult to interpret [3]. The reliability of a neural network is directly connected to the quality of the internal data representation that it has built.