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Abstract
This article is concerned with spectro-temporal (i.e., time varying spectrum) analysis of ECG signals for application in atrial fibrillation (AF) detection. We propose a Bayesian spectro-temporal representation of ECG signal using state-space model and Kalman filter. The 2D spectro-temporal data are then classified by a densely connected convolutional networks (DenseNet) into four different classes: AF, non-AF normal rhythms (Normal), non-AF abnormal rhythms (Others), and noisy segments (Noisy). The performance of the proposed algorithm is evaluated and scored with the PhysioNet/Computing in Cardiology (CinC) 2017 dataset. The experiment results shows that the proposed method achieves the overall F1 score of 80.2%, which is in line with the state-of-the-art algorithms. In addition, the proposed spectro-temporal estimation approach outperforms standard time-frequency analysis methods, that is, short-time Fourier transform, continuous wavelet transform, and autoregressive spectral estimation for AF detection.
Original language | English |
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Title of host publication | 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 |
Editors | Nelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 978-1-5386-5477-4 |
DOIs | |
Publication status | Published - 2018 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE International Workshop on Machine Learning for Signal Processing - Aalborg, Denmark Duration: 17 Sept 2018 → 20 Sept 2018 Conference number: 28 |
Publication series
Name | IEEE International Workshop on Machine Learning for Signal Processing |
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Publisher | IEEE |
ISSN (Print) | 2161-0363 |
ISSN (Electronic) | 2161-0371 |
Workshop
Workshop | IEEE International Workshop on Machine Learning for Signal Processing |
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Abbreviated title | MLSP |
Country/Territory | Denmark |
City | Aalborg |
Period | 17/09/2018 → 20/09/2018 |
Keywords
- atrial fibrillation
- deep learning
- Kalman filter
- state-space model
- spectrogram estimation
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Dive into the research topics of 'Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection'. Together they form a unique fingerprint.Projects
- 1 Finished
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Robust Computational ECG Methods for Automated Diagnosis of Cardiac Diseases from Long-Term Recordings
Palva, L., Suotsalo, K., Särkkä, S., Bahrami Rad, A., Hostettler, R., Tronarp, F., Sarmavuori, J., Zhao, Z. & Karvonen, T.
01/06/2016 → 31/12/2018
Project: Business Finland: Other research funding