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Article

XECGARCH: A TRUSTWORTHY DEEP LEARNING ARCHITECTURE FOR INTERPRETABLE ECG
ANALYSIS CONSIDERING SHORT-TERM AND LONG-TERM FEATURES

Marc Goettling, Alexander Hammer, Hagen Malberg, Martin Schmidt



This is a preprint; it has not been peer reviewed by a journal.



https://doi.org/10.21203/rs.3.rs-3654418/v1

This work is licensed under a CC BY 4.0 License


STATUS:

Under Review






VERSION 1

posted 18 Apr, 2024

5

You are reading this latest preprint version


ABSTRACT



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 = 9,854 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.

Health sciences/Cardiology

Physical sciences/Engineering/Biomedical engineering

Physical sciences/Mathematics and computing/Computer science


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STATUS:

Under Review






VERSION 1

posted 18 Apr, 2024



 * Reviewers agreed at journal
   
   18 Apr, 2024

 * Reviewers agreed at journal
   
   19 Feb, 2024

 * Reviewers invited by journal
   
   29 Jan, 2024

 * Submission checks completed at journal
   
   28 Jan, 2024

 * First submitted to journal
   
   23 Jan, 2024

You are reading this latest preprint version


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