xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases ( n = 9854 ECGs) and achieved an F1 score of 95.43% in AF versus non-AF classification on an unseen ECG test dataset. A systematic comparison of xAI methods showed that deep Taylor decomposition provided the most trustworthy explanations ( + 24 % compared to the second-best approach). xECGArch can account for short- and long-term features corresponding to clinical features of morphology and rhythm, respectively. Further research will focus on the relationship between xECGArch features and clinical features, which may help in medical applications for diagnosis and therapy.

Titel in Übersetzung
xECGArch: Eine vertrauenswürdige Deep-Learning-Architektur für die interpretierbare EKG-Analyse unter Berücksichtigung von Kurz- und Langzeitmerkmalen


FachzeitschriftScientific Reports
PublikationsstatusVeröffentlicht - 7 Juni 2024

Externe IDs

ORCID /0000-0003-4012-0608/work/161407073
ORCID /0000-0002-1984-580X/work/161410060
PubMed 38849417
Scopus 85195533830


Ziele für nachhaltige Entwicklung


  • Neural Networks, Computer, Algorithms, Electrocardiography/methods, Humans, Artificial Intelligence, Atrial Fibrillation/diagnosis, Deep Learning, Databases, Factual