Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review

Matias, Igor; García, Nuno M.; Pirbhulal, Sandeep; Felizardo, Virginie; Pombo, Nuno; Zacarias, Henriques; Sousa, Miguel; Zdravevski, Eftim

doi:10.1016/j.cosrev.2020.100334

Cited by 26 publications

(14 citation statements)

References 23 publications

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“…Following the second P related to Prevention Medicine, the medical and clinical healthrelated data sets, which are the prime concerns in healthcare decision-making, will be used on preventive models development. In addition, these models will be augmented with additional non-clinical background features to enhance the predictive capabilities of the preventive models [71]. With the method developed, the goal is to change people's behaviors or take adequate actions before healthcare problems occur.…”

Section: Research Backgroundmentioning

confidence: 99%

Mobile 5P-Medicine Approach for Cardiovascular Patients

Pires

Denysyuk

Villasana

et al. 2021

Sensors

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Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

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Section: Research Backgroundmentioning

confidence: 99%

Mobile 5P-Medicine Approach for Cardiovascular Patients

Pires

Denysyuk

Villasana

et al. 2021

Sensors

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“… 5 , 6 They are also able to help detect or even predict health issues by the mean of more advanced measurements like an electrocardiogram (ECG). 7 When combining wearable ECG signals with artificial intelligence algorithms, illness prediction is possible, 8 transforming these ubiquitous and accessible devices into a powerful source of self-information.…”

Section: Introductionmentioning

confidence: 99%

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

2022

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Background Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual. Objective Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population. Methods We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life. Results We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate. Conclusions This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

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“…In the last few years, machine learning (ML) approaches have gained much interest for the analysis of medical signals and images [ 11 – 16 ]. Deep learning (DL) pipelines are kinds of ML and have reached better feature extraction and classification outcomes compared to the state-of-the-art performance in the different fields of computer vision tasks [ 17 – 19 ].…”

Section: Introductionmentioning

confidence: 99%

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

Sarshar

Mirzaei

2022

Journal of Healthcare Engineering

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Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.

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Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review

Cited by 26 publications

References 23 publications

Mobile 5P-Medicine Approach for Cardiovascular Patients

Mobile 5P-Medicine Approach for Cardiovascular Patients

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

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