Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection

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Research units


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 languageEnglish
Title of host publication2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
EditorsNelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Aalborg, Denmark
Duration: 17 Sep 201820 Sep 2018
Conference number: 28

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371


WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP

    Research areas

  • atrial fibrillation, deep learning, Kalman filter, state-space model, spectrogram estimation

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