Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

• Published in: Heliyon Vol. 9; no. 2; p. e13601
• Main Authors: Denysyuk, Hanna Vitaliyivna, Pinto, Rui João, Silva, Pedro Miguel, Duarte, Rui Pedro, Marinho, Francisco Alexandre, Pimenta, Luís, Gouveia, António Jorge, Gonçalves, Norberto Jorge, Coelho, Paulo Jorge, Zdravevski, Eftim, Lameski, Petre, Leithardt, Valderi, Garcia, Nuno M., Pires, Ivan Miguel
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2023; Elsevier
• Subjects: Cardiovascular diseases; Diagnosis; ECG sensors; Review; Systematic review; POAF; CNN; BNN; ECG sensors; DNN; LR; DL; LDA; Systematic review; SVM; GRU; MLP; MLR; NLP; RNN; LASSO; Diagnosis; WHO; ML; SHAP; LSTM; GMM; ECG; AI; kNN; RF; GAN; GNB; Cardiovascular diseases; SVM, Support Vector Machine; ML, Machine Learning; LDA, Linear Discriminant Analysis; ECG, Electrocardiography; GAN, Generative Adversarial Networks; LR, Linear Regression; DL, Deep Learning; BNN, Binarized Neural Network; LSTM, Long Short-Term Memory; GNB, Gaussian Naive bayes; SHAP, SHapley Additive exPlanations; GRU, Gated Recurrent Unit; MLR, Multiple Linear Regression; AI, Artificial Intelligence; WHO, World Health Organization; POAF, Postoperative Atrial Fibrillation; GMM, Gaussian Mixture Model; RF, Random Forest; MLP, Multiplayer Perceptron; kNN, k-nearest neighbors; LASSO, Least Absolute Shrinkage and Selection Operator; DNN, Deep Neural Networks; NLP, Natural Language Processing; CNN, Concolutional Neural Networks; RNN, Recurrent Neural Network
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e13601

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.