A deep learning approach for real-time detection of atrial fibrillation

By Rasmus S. Andersen, Abdolrahman Peimankar, Sadasivan K. Puthusserypady. Expert systems with applications, 2019

358 citations recorded by the source corpus.

Abstract Goal: To develop a robust and real-time approach for automatic detection of atrial fibrillation (AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An end-to-end model combining the Convolutional- and Recurrent-Neural Networks (CNN and RNN) was proposed to extract high level features from segments of RR intervals (RRIs) in order to classify them as AF or normal sinus rhythm (NSR). Results: The model was trained and validated on three different databases including a total of 89 subjects. It achieved a sensitivity and specificity of 98.98% and 96.95%, respectively, validated through a 5-fold cross-validation. Additionally, the proposed model was found to be computationally efficient and it was capable of analyzing 24 h of ECG recordings in less than one second. The proposed algorithm was also tested on the unseen datasets to examine its robustness in detecting AF for new recordings which resulted in 98.96% and 86.04% for specificity and sensitivity, respectively. Conclusion: Compared to the state-of-the-art models evaluated on standard benchmark ECG datasets, the proposed model produced better performance in detecting AF. Additionally, since the model learns features directly from the data, it avoids the need for clever/cumbersome feature engineering.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f6205219d686bf0ec1491dc0a91388a345345dec

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

A review on deep learning methods for ECG arrhythmia classification (2020, neutral): “c Atrial Fibrillation (AF) (Savalia & Emamian, 2018), (Andersen et al., 2019), (Rubin et al.”
A review on the long short-term memory model (2020, methodology): “The authors in Andersen et al. (2019) developed an approach for automatic detection of atrial fibrillation. These applications all apply combinations of network types, where LSTM learns the temporal relations in the data. On the other hand, after several data preprocessing steps, LSTM is part of the”