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.
|Title of host publication||2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings|
|Publication date||May 2019|
|Publication status||Published - May 2019|
|Event||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom|
Duration: 12. May 2019 → 17. May 2019
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||12/05/2019 → 17/05/2019|
|Sponsor||The Institute of Electrical and Electronics Engineers Signal Processing Society|
|Series||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
Bibliographical noteFunding Information:
This work is supported by the Innovation Fund Denmark (REAFEL, IFD Project No: 6153-00009B).
- Deep learning
- Ensemble learning
- Long-Short Term Memory
- P-waves detection