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

Abstract

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.

Keywords: AI, Artificial Intelligence; BNN, Binarized Neural Network; CNN, Concolutional Neural Networks; Cardiovascular diseases; DL, Deep Learning; DNN, Deep Neural Networks; Diagnosis; ECG sensors; ECG, Electrocardiography; GAN, Generative Adversarial Networks; GMM, Gaussian Mixture Model; GNB, Gaussian Naive bayes; GRU, Gated Recurrent Unit; LASSO, Least Absolute Shrinkage and Selection Operator; LDA, Linear Discriminant Analysis; LR, Linear Regression; LSTM, Long Short-Term Memory; ML, Machine Learning; MLP, Multiplayer Perceptron; MLR, Multiple Linear Regression; NLP, Natural Language Processing; POAF, Postoperative Atrial Fibrillation; RF, Random Forest; RNN, Recurrent Neural Network; SHAP, SHapley Additive exPlanations; SVM, Support Vector Machine; Systematic review; WHO, World Health Organization; kNN, k-nearest neighbors.

Fig. 1

Flow diagram of identification and inclusion of papers.

Fig. 2

Distribution of studies by the country where the studies were conducted.

Fig. 3

Distribution of studies by disease.

Fig. 4

Distribution of the various machine learning methods by the studies.