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/

Current and Future Use of Artificial Intelligence in Electrocardiography.

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.Manuel Marina-Breysse has received funding from European Union’s Horizon 2020 research and innovation program under the grant agreement number 965286; Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation, MAESTRIA Consortium; and EIT Health, a body of the European Union.S

Reliability of wavelet analysis of heart rate variability during rest and exercise

The reliability of wavelet analysis (DWT), of heart rate variability during rest and exercise was examined for this study. All twenty-three participants visited the lab on two separate occasions no less than four weeks apart. All twenty-three participants were subjected to the spontaneous breathing (SB1), and HG60 exercise condition. Of those twenty-three participants, nine performed the HG20 exercise condition as well. It was found that during the SB1 condition, the R-R intervals were fairly reliable between days. However, the reliability of all the HRV parameters (SDNN, spectral components and wavelet components) were quite poor. Interestingly, however, during HG20, the reliability of the HRV parameters was much more promising. The ability of DWT to detect changes in sympathovagal balance with incremental handgrip exercise was seen, despite a very low number of participants

Myocardial t1 Mapping Techniques for Quantification of Myocardial Fibrosis

Identifying and quantifying diffuse myocardial fibrosis is important to provide insights into the relationship between myocardial fibrosis, diastolic and systolic dysfunction, as well as clinical outcomes. T1 mapping is a promising technique for noninvasively identifying diffuse myocardial fibrosis in heart failure. A quantitative T1 map provides sensitivity to the full range of T1 values and is advantageous over the traditional T1-weighted imaging by reducing the reliance on visual interpretation of the signal intensity in the myocardium. However, in-vivo myocardial T1 quantification is challenging because of cardiac and respiratory motion. During the past few years, a variety of T1 mapping techniques, including the modified Look Locker inversion recovery (MOLLI) sequence, have been developed and optimized to measure the myocardial T1 value. Importantly, there have been significant differences between the T1 values determined by various methods, and several aspects of T1 mapping are incompletely understood. The accuracy of T1 mapping is sensitive to several confounding factors, such as the types of T1 mapping acquisition sequence and individual physiologic parameters. It also remains unclear if myocardial T1 values are constant throughout the cardiac cycle or the cyclic variation from the error of the variable flip angle (VFA) technique. Lastly, it is necessary to validate these techniques against the endomyocardial biopsy. The work intends to validate several aspects of T1 mapping. Firstly, whether there is significant cyclic variation of myocardial T1 at 1.5T was assessed in healthy volunteers and patients without myocardial disease. Secondly, a fast 3D DFA technique with B1 correction was developed to measure T1 comparably with gold standard in a wide range of T1 values, which showed it is necessary to incorporate B1 correction at 3T. Thirdly, Look Locker and MOLLI were compared to evaluate their agreement and difference in 3 patient groups precontrast and postcontrast situations. Finally, the T1 mapping te

Applying Artificial Intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review

Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may fac