Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection

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Conference article in proceedings
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Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018, IEEE International Workshop on Machine Learning for Signal Processing
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.
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Keywords
atrial fibrillation, deep learning, Kalman filter, state-space model, spectrogram estimation
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Citation
Zhao , Z , Särkkä , S & Bahrami Rad , A 2018 , Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection . in N Pustelnik , Z-H Tan , Z Ma & J Larsen (eds) , 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 . IEEE International Workshop on Machine Learning for Signal Processing , IEEE , IEEE International Workshop on Machine Learning for Signal Processing , Aalborg , Denmark , 17/09/2018 . https://doi.org/10.1109/MLSP.2018.8517085