An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram

Abdolrahman Peimankar, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Detection of P-waves in electrocardiogram (ECG) signals is of great importance to cardiologists in order to help them diagnosing arrhythmias such as atrial fibrillation. This paper proposes an end-to-end deep learning approach for detection of P-waves in ECG signals. Four different deep Recurrent Neural Networks (RNNs), namely, the Long-Short Term Memory (LSTM) are used in an ensemble framework. Each of these networks are trained to extract the useful features from raw ECG signals and determine the absence/presence of P-waves. Outputs of these classifiers are then combined for final detection of the P-waves. The proposed algorithm was trained and validated on a database which consists of more than 111000 annotated heart beats and the results show consistently high classification accuracy and sensitivity of around 98.48% and 97.22%, respectively.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherIEEE
Publication dateMay 2019
Pages1284-1288
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12. May 201917. May 2019

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
SponsorThe Institute of Electrical and Electronics Engineers Signal Processing Society
SeriesICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN1520-6149

Bibliographical note

Funding Information:
This work is supported by the Innovation Fund Denmark (REAFEL, IFD Project No: 6153-00009B).

Keywords

  • Deep learning
  • Electrocardiogram
  • Ensemble learning
  • Long-Short Term Memory
  • P-waves detection

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