Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Journal: PloS one
PMID: 31107876

Abstract

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

Authors

  • Artzai Picon
    Computer Vision Group, Tecnalia Research & Innovation, Derio, Spain.
  • Unai Irusta
    Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.
  • Aitor Álvarez-Gila
    Computer Vision Group, Tecnalia Research & Innovation, Derio, Spain.
  • Elisabete Aramendi
    Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.
  • Felipe Alonso-Atienza
    Department of Telecommunication Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
  • Carlos Figuera
    Department of Telecommunication Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
  • Unai Ayala
    Electronics and Computing Department, University of Mondragon, Mondragon, Spain.
  • Estibaliz Garrote
    Computer Vision Group, Tecnalia Research & Innovation, Derio, Spain.
  • Lars Wik
    Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Oslo University Hospital and University of Oslo, Oslo, Norway.
  • Jo Kramer-Johansen
    Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Oslo University Hospital and University of Oslo, Oslo, Norway.
  • Trygve Eftestøl
    Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway.

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