ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition

Abstract

Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.

Application of Artificial Neural Networks in the Identification of Heart Abnormalities Based on Ecg: Literature Review

“…From the explanation above, research related to ECG with an artificial neural network approach can be presented in Table 5. [68] ANN (Levenberg-Marquardt) PhysioNet 2021 [2] ANN 2021 [69] Multilayered Perceptron Accuracy: 98,89 % PhysioNet 2022 [3] ANN Accuracy: 92,47 % UCI 2022 [70] Backpropagation Accuracy: 87 % 2022 [71] Multi-Layer Perceptron PhysioNet 2022 [39] CNN Accuracy: 92 % PhysioNet 2022 [72] Convolution Neural Networks Cardiovascular Department of Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI) in Trieste, Italy…”