Evolutionary Optimization of Atrial Fibrillation Diagnostic Algorithms

The goal of this research is to introduce an improved method for detecting atrial fibrillation (AF). The foundation of our algorithm is the irregularity of the RR intervals in the electrocardiogram (ECG) signal, and their correlation with AF. Three statistical techniques, including root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE), are used to detect RR interval irregularity. We use the Massachusetts Institution of Technology / Beth Israel Hospital (MIT-BIH) atrial fibrillation databases and their annotations to tune the parameters of the statistical methods by biogeography-based optimization (BBO), which is an evolutionary optimization algorithm. We trained each statistical method to diagnose AF on each database. Then each trained method was tested on the rest of the databases. We were able to obtain accuracy levels as high as 99 for the detection of AF in the trained databases. We obtained accuracy levels of up to 75 in the tested database

Arrhythmia Evaluation in Wearable ECG Devices

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.Cal-Comp Electronics & Communications Co., Ltd.; Kinpo Electronics, Inc. New Taipei City, Taiwan. Innovation Center for Big Data and Digital Convergence; Yuan Ze University, Taiwan

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

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.N/