Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection

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  • Deepaware

    Final published version, 1.81 MB, PDF document

  • Devender Kumar
  • Abdolrahman Peimankar
  • Kamal Sharma
  • Dominguez, Helena
  • Sadasivan Puthusserypady
  • Jakob E. Bardram

Background: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a significantly high false positive rates (FPRs) when applied on ECG data collected under free-living ambulatory conditions and in the presence of non-AF arrhythmias. Method: This paper proposes DeepAware, a novel hybrid model combining deep learning (DL) and context-aware heuristics (CAH), which reduces the FPR effectively and improves the AF detection performance on participant-operated ambulatory ECG from free-living conditions. It exploits the RRI and P-wave features, as well as the contextual features from the ambulatory ECG. Results: DeepAware is shown to be very generalizable and superior to the state-of-the-art models when applied on unseen benchmark ECG AF datasets. Most importantly, the model is able to detect AF efficiently when applied on participant-operated ambulatory ECG recordings from free-living conditions and has achieved a sensitivity (Se), specificity (Sp), and accuracy (Acc) of 97.94%, 98.39%, 98.06%, respectively. Results also demonstrate the effect of atrial activity analysis (via P-waves detection) and CAH in reducing the FPR over the RRI features-based AF detection model. Conclusions: The proposed DeepAware model can substantially reduce the physician's workload of manually reviewing the false positives (FPs) and facilitate long-term ambulatory monitoring for early detection of AF.

Original languageEnglish
Article number106899
JournalComputer Methods and Programs in Biomedicine
Number of pages11
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

    Research areas

  • Arrhythmia, Atrial fibrillation, Context-awareness, Convolutional neural networks, Deep learning, Electrocardiogram (ECG), Health informatics, Long short-term memory (LSTM)

ID: 314072581