Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias

State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology

AIMS

Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field.

METHODS AND RESULTS

An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP.

CONCLUSION

The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Evaluation of Spandan Smartphone-Based Electrocardiogram for Arrhythmia Detection: A Cross-Sectional Study in a Large Patient Cohort

BACKGROUND

To assess the diagnostic accuracy of the Spandan Lead II smartphone-based electrocardiogram (ECG) device regarding cardiac arrhythmia, compared with that of the only lead II ECG strip from the gold-standard ECG machine (BPL ECG machine) and the diagnosis by a cardiologist.

METHODS

The study, conducted from August 2, 2022, to June 2, 2023, in the local hospital, included 2799 participants aged 20 years and above. This was a single-blinded, cross-sectional study comparing the Spandan ECG device against the Gold Standard ECG and was diagnosed by a cardiologist. Participants referred for ECG testing by a cardiologist were included, and those with a pacemaker and/or ECG artifacts were excluded. To avoid any bias, the diagnosis was blinded to the cardiologist. Sensitivity, specificity, predictive values, F-score, and Matthew's correlation coefficient of the Spandan device were the parameters on which accuracy was studied.

RESULTS

Among 2799 participants (843 females, 1,956 males), the Spandan ECG system demonstrated high accuracy compared to the gold standard ECG machine, with sensitivity (95.5%), specificity (96.3%), positive predictive value (93.2%), negative predictive value (97.6%), F-Score (0.94), and a P = .913, for P > .001. It identified all arrhythmias without discrepancies and closely aligned with the gold standard ECG, which had slightly lower performance metrics. The study concluded that the Spandan Lead II ECG system is clinically applicable, especially in resource-limited settings.

CONCLUSION

The Spandan lead II smartphone-based ECG device offers high accuracy in diagnosing cardiac arrhythmias, comparable to standard ECG machines. Its portability, affordability, and ease of use make it a valuable tool for timely diagnosis in almost all clinical and non-clinical settings.

Prediction of sudden cardiac death using artificial intelligence: Current status and future directions

Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among people who suffer a SCD, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators for SCD prevention. In response, artificial intelligence (AI) holds promise for personalized SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate nonlinear patterns between complex data and defined end points, and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.

Artificial intelligence driven clustering of blood pressure profiles reveals frailty in orthostatic hypertension

Gravity, an invisible but constant force , challenges the regulation of blood pressure when transitioning between postures. As physiological reserve diminishes with age, individuals grow more susceptible to such stressors over time, risking inadequate haemodynamic control observed in orthostatic hypotension. This prevalent condition is characterized by drops in blood pressure upon standing; however, the contrary phenomenon of blood pressure rises has recently piqued interest. Expanding on the currently undefined orthostatic hypertension, our study uses continuous non-invasive cardiovascular data to explore the full spectrum of blood pressure profiles and their associated frailty outcomes in community-dwelling older adults. Given the richness of non-invasive beat-to-beat data, artificial intelligence (AI) offers a solution to detect the subtle patterns within it. Applying machine learning to an existing dataset of community-based adults undergoing postural assessment, we identified three distinct clusters (iOHYPO, OHYPO and OHYPER) akin to initial and classic orthostatic hypotension and orthostatic hypertension, respectively. Notably, individuals in our OHYPER cluster exhibited indicators of frailty and sarcopenia, including slower gait speed and impaired balance. In contrast, the iOHYPO cluster, despite transient drops in blood pressure, reported fewer fallers and superior cognitive performance. Surprisingly, those with sustained blood pressure deficits outperformed those with sustained rises, showing greater independence and higher Fried frailty scores. Working towards more refined definitions, our research indicates that AI approaches can yield meaningful blood pressure morphologies from beat-to-beat data. Furthermore, our findings support orthostatic hypertension as a distinct clinical entity, with frailty implications suggesting that it is worthy of further investigation.

Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems

BACKGROUND

At a critical juncture in the ongoing fight against cardiovascular disease (CVD), healthcare professionals are striving for more informed and expedited decisionmaking. Artificial intelligence (AI) promises to be a guiding light in this endeavor. The diagnosis of coronary artery disease has now become non-invasive and convenient, while wearable devices excel at promptly detecting life-threatening arrhythmias and treatments for heart failure.

OBJECTIVE

This study aimed to highlight the applications of AI in cardiology with a particular focus on arrhythmias and its potential impact on healthcare for all through careful implementation and constant research efforts.

METHODS

An extensive search strategy was implemented. The search was conducted in renowned electronic medical databases, including Medline, PubMed, Cochrane Library, and Google Scholar. Artificial Intelligence, cardiovascular diseases, arrhythmias, machine learning, and convolutional neural networks in cardiology were used as keywords for the search strategy.

RESULTS

A total of 6876 records were retrieved from different electronic databases. Duplicates (N = 1356) were removed, resulting in 5520 records for screening. Based on predefined inclusion and exclusion criteria, 4683 articles were excluded. Following the full-text screening of the remaining 837 articles, a further 637 were excluded. Ultimately, 200 studies were included in this review.

CONCLUSION

AI represents not just a development but a cutting-edge force propelling the next evolution of cardiology. With its capacity to make precise predictions, facilitate non-invasive diagnosis, and personalize therapies, AI holds the potential to save lives and enhance healthcare quality on a global scale.

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias

PURPOSE OF REVIEW

To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias.

RECENT FINDINGS

Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias.

A predictive score for atrial fibrillation in poststroke patients

BACKGROUND

 Atrial fibrillation (AF) is a risk factor for cerebral ischemia. Identifying the presence of AF, especially in paroxysmal cases, may take time and lacks clear support in the literature regarding the optimal investigative approach; in resource-limited settings, identifying a higher-risk group for AF can assist in planning further investigation.

OBJECTIVE

 To develop a scoring tool to predict the risk of incident AF in the poststroke follow-up.

METHODS

 A retrospective longitudinal study with data collected from electronic medical records of patients hospitalized and followed up for cerebral ischemia from 2014 to 2021 at a tertiary stroke center. Demographic, clinical, laboratory, electrocardiogram, and echocardiogram data, as well as neuroimaging data, were collected. Stepwise logistic regression was employed to identify associated variables. A score with integer numbers was created based on beta coefficients. Calibration and validation were performed to evaluate accuracy.

RESULTS

 We included 872 patients in the final analysis. The score was created with left atrial diameter ≥ 42 mm (2 points), age ≥ 70 years (1 point), presence of septal aneurysm (2 points), and score ≥ 6 points at admission on the National Institutes of Health Stroke Scale (NIHSS; 1 point). The score ranges from 0 to 6. Patients with a score ≥ 2 points had a fivefold increased risk of having AF detected in the follow-up. The area under the curve (AUC) was of 0.77 (0.72-0.85).

CONCLUSION

 We were able structure an accurate risk score tool for incident AF, which could be validated in multicenter samples in future studies.

The potential for large language models to transform cardiovascular medicine

Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their outputs can provide rich information that otherwise would not be detected by cardiologists. With recent advances in transformer models, multimodal AI, and large language models, the ability to integrate electronic health record data with images, genomics, biosensors, and other data has the potential to improve diagnosis and partition patients who are at high risk for primary preventive strategies. Although much emphasis has been placed on AI supporting clinicians, AI can also serve patients and provide immediate help with diagnosis, such as that of arrhythmia, and is being studied for automated self-imaging. Potential risks, such as loss of data privacy or potential diagnostic errors, should be addressed before use in clinical practice. This Series paper explores opportunities and limitations of AI models for cardiovascular medicine, and aims to identify specific barriers to and solutions in the application of AI models, facilitating their integration into health-care systems.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment

The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6 ± 4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.

The potential role for artificial intelligence in fracture risk prediction

Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI-ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI-ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI-ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Real-Time Cardiac Abnormality Monitoring and Nursing for Patient Using Electrocardiographic Signals

INTRODUCTION

Cardiovascular disease nursing is a critical clinical application that necessitates real-time monitoring models. Previous models required the use of multi-lead signals and could not be customized as needed. Traditional methods relied on manually designed supervised algorithms, based on empirical experience, to identify waveform abnormalities and classify diseases, and were incapable of monitoring and alerting abnormalities in individual waveforms.

METHODS

This research reconstructed the vector model for arbitrary leads using the phase space-time-delay method, enabling the model to arbitrarily combine signals as needed while possessing adaptive denoising capabilities. After employing automatically constructed machine learning algorithms and designing for rapid convergence, the model can identify abnormalities in individual waveforms and classify diseases, as well as detect and alert on abnormal waveforms.

RESULT

Effective noise elimination was achieved, obtaining a higher degree of loss function fitting. After utilizing the algorithm in Section 3.1 to remove noise, the signal-to-noise ratio increased by 8.6%. A clipping algorithm was employed to identify waveforms significantly affected by external factors. Subsequently, a network model established by a generative algorithm was utilized. The accuracy for healthy patients reached 99.2%, while the accuracy for APB was 100%, for LBBB 99.32%, for RBBB 99.1%, and for P-wave peak 98.1%.

CONCLUSION

By utilizing a three-dimensional model, detailed variations in electrocardiogram signals associated with different diseases can be observed. The clipping algorithm is effective in identifying perturbed and damaged waveforms. Automated neural networks can classify diseases and patient identities to facilitate precision nursing.

The potential of artificial intelligence to revolutionize health care delivery, research, and education in cardiac electrophysiology

The field of electrophysiology (EP) has benefited from numerous seminal innovations and discoveries that have enabled clinicians to deliver therapies and interventions that save lives and promote quality of life. The rapid pace of innovation in EP may be hindered by several challenges including the aging population with increasing morbidity, the availability of multiple costly therapies that, in many instances, confer minor incremental benefit, the limitations of healthcare reimbursement, the lack of response to therapies by some patients, and the complications of the invasive procedures performed. To overcome these challenges and continue on a steadfast path of transformative innovation, the EP community must comprehensively explore how artificial intelligence (AI) can be applied to healthcare delivery, research, and education and consider all opportunities in which AI can catalyze innovation; create workflow, research, and education efficiencies; and improve patient outcomes at a lower cost. In this white paper, we define AI and discuss the potential of AI to revolutionize the EP field. We also address the requirements for implementing, maintaining, and enhancing quality when using AI and consider ethical, operational, and regulatory aspects of AI implementation. This manuscript will be followed by several perspective papers that will expand on some of these topics.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Correlation between serum uric acid level and atrial fibrillation in patients with hyperthyroidism on medical data analysis context of IoT

BACKGROUND

Dysfunctions in metabolism and endocrine systems are outcomes of disruptions in human physiological processes, often leading to disease onset. External factors can hinder the human body's innate capacity for self-regulation and healing, particularly when immune responses are compromised, allowing these factors to interfere with normal bodily functions directly.

OBJECTIVE

To explore the effect of uric acid expression water in blood on the occurrence of atrial fibrillation in patients with hyperthyroidism, the expression level of uric acid in the blood and other physiological indexes were compared between patients with no symptoms of atrial fibrillation and patients with hyperthyroidism with symptoms of atrial fibrillation, to find the correlation between them.

METHODS

A group of 112 hyperthyroidism patients who were admitted to our hospital from September 2019 to March 2020 were chosen and split into two groups. The control group consisted of 56 individuals (21 men and 35 women) aged between 16 and 86 years old, with an average age of 46.23 years (± 7.63). The observation group consisted of 56 individuals (24 males and 32 females) between 15 and 79 years, with an average age of 53.44 years (± 8.91).

RESULTS

In the patients who were not treated with drugs before hospitalization the disease course and symptoms varied. The patients' clinical medical and demographic data were recorded and the patients' physiological indexes were obtained through blood tests and analysis. The differences between the two groups were analyzed by renal function, blood lipid index, thyroid function, and cardiac ultrasound, and these influencing factors were analyzed by regression analysis. The research adhered to ethical norms and ensured clear data presentation by using a rigorous technique to compare uric acid levels and physiological indicators among various patient groups.

CONCLUSION

The study concentrated on the validation, repeatability, and contextual interpretation of data to provide a robust and rigorously scientific comparison. The most common is the increase of uric acid in the blood, which can induce other diseases, and atrial fibrillation is one of the most common diseases of cardiovascular diseases.