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

Abdolrahman Peimankar, Sadasivan Puthusserypady

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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)
PublisherIEEE
Publication date2019
Pages1284-1288
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - 2019
Event2019 IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton Conference Centre, Brighton, United Kingdom
Duration: 12 May 201917 May 2019
Conference number: 44
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech, and Signal Processing
Number44
LocationBrighton Conference Centre
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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