Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Abstract

Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

“…Another future work consists in the protection of sensitive information that may be contained in the different types of ECG segments considered in this study [14]. As we observed, the Autoencoder of ECGXtractor was not trained specifically for recognition tasks.…”

“…With these four databases, we evaluate the impact of different ECG properties on biometric recognition. We use an in-house database [14], [42], and three public databases widely used in the literature, to compare the performances achieved in our experiments with other studies. The main characteristics of the databases are reported in Table II.…”

“…First of all, ECGs provide higher security as the signal is measured inside the body, which is therefore difficult to simulate or copy [13]. ECGs allow liveness detection, as they can be captured from living subjects only, and provide useful additional information related to psychological states and clinical status [14]. The possibility to capture ECGs from fingers [15] or through wearable devices [16] simplifies their acquisition and increases the acceptability of ECG signals for commercial and public applications [17].…”

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

“…First of all, ECGs provide higher security as the signal is measured inside the body, which is therefore dicult to simulate or copy [14]. ECGs allow liveness detection, as they can be captured from living subjects only, and provide useful additional information related to psychological states and clinical status [15]. Recently, it has been claimed that unconcious patients can be identied by their cardiac activity [16].…”

An Overview of Privacy-enhancing Technologies in Biometric Recognition

“…In addition, further information can be derived from the biometric data, including health conditions, emotions, softbiometric attributes, and other personal aspects [12], [13]. Also, we note that storing processed biometric data (e.g.…”