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 language | English |
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Title of host publication | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Publisher | IEEE |
Publication date | 2019 |
Pages | 1284-1288 |
ISBN (Electronic) | 978-1-4799-8131-1 |
DOIs | |
Publication status | Published - 2019 |
Event | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton Conference Centre, Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44 https://2019.ieeeicassp.org/ https://www.2019.ieeeicassp.org/2019.ieeeicassp.org/index.html |
Conference
Conference | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing |
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Number | 44 |
Location | Brighton Conference Centre |
Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/2019 → 17/05/2019 |
Sponsor | IEEE |
Internet address |
Series | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
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ISSN | 1520-6149 |