Conference paper
An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram
Department of Health Technology, Technical University of Denmark1
Digital Health, Department of Health Technology, Technical University of Denmark2
Brain Computer Interface, Digital Health, Department of Health Technology, Technical University of Denmark3
Copenhagen Center for Health Technology, Centers, Technical University of Denmark4
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 1284-1288 |
Proceedings: | 2019 IEEE International Conference on Acoustics, Speech, and Signal ProcessingIEEE International Conference on Acoustics, Speech and Signal Processing |
Series: | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
ISBN: | 1479981311 , 147998132X , 147998132x , 9781479981311 and 9781479981328 |
ISSN: | 2379190x and 15206149 |
Types: | Conference paper |
DOI: | 10.1109/ICASSP.2019.8682307 |
ORCIDs: | Peimankar, Abdolrahman and Puthusserypady, Sadasivan |
Deep learning Electrocardiogram Electrocardiography Ensemble learning Feature extraction Heart beat LSTM Logic gates Long-Short Term Memory P-wave detection P-waves P-waves detection Rail to rail inputs Recurrent neural networks Training arrhythmias atrial fibrillation deep recurrent neural networks electrocardiogram signals electrocardiography end-to-end deep learning approach ensemble framework learning (artificial intelligence) medical signal processing raw ECG signals recurrent neural nets