Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection

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A4 Artikkeli konferenssijulkaisussa
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Date

2018

Department

Department of Electrical Engineering and Automation

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Mcode

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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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DOI

10.1109/MLSP.2018.8517085

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

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https://urn.fi/URN:NBN:fi:aalto-201901141103

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[article-cris] Sähkötekniikan korkeakoulu / ELEC