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

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)
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
CountryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Cite this

Peimankar, A., & Puthusserypady, S. (2019). An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1284-1288). IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2019.8682307
Peimankar, Abdolrahman ; Puthusserypady, Sadasivan. / An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. pp. 1284-1288 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).
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title = "An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram",
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.",
author = "Abdolrahman Peimankar and Sadasivan Puthusserypady",
year = "2019",
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Peimankar, A & Puthusserypady, S 2019, An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram. in 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, pp. 1284-1288, 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019. https://doi.org/10.1109/ICASSP.2019.8682307

An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram. / Peimankar, Abdolrahman; Puthusserypady, Sadasivan.

2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. p. 1284-1288 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

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

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N2 - 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.

AB - 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.

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Peimankar A, Puthusserypady S. An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2019. p. 1284-1288. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). https://doi.org/10.1109/ICASSP.2019.8682307