Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Zhao, Zheng
dc.contributor.author Särkkä, Simo
dc.contributor.author Bahrami Rad, Ali
dc.contributor.editor Pustelnik, Nelly
dc.contributor.editor Tan, Zheng-Hua
dc.contributor.editor Ma, Zhanyu
dc.contributor.editor Larsen, Jan
dc.date.accessioned 2019-01-14T09:19:30Z
dc.date.available 2019-01-14T09:19:30Z
dc.date.issued 2018
dc.identifier.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 , Institute of Electrical and Electronics Engineers , IEEE International Workshop on Machine Learning for Signal Processing , Aalborg , Denmark , 17/09/2018 . DOI: 10.1109/MLSP.2018.8517085 en
dc.identifier.isbn 978-1-5386-5477-4
dc.identifier.issn 2161-0363
dc.identifier.issn 2161-0371
dc.identifier.other PURE UUID: 1f9fec41-5ee7-4520-b8d8-a6ab103fd94d
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/spectrotemporal-ecg-analysis-for-atrial-fibrillation-detection(1f9fec41-5ee7-4520-b8d8-a6ab103fd94d).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/27863063/ELEC_Zhao_etal_Spectro_Temporal_ECG_Analysis_MLSP_2018.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35920
dc.description.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. en
dc.format.extent 6
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE
dc.relation.ispartof IEEE International Workshop on Machine Learning for Signal Processing en
dc.relation.ispartofseries 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 en
dc.relation.ispartofseries IEEE International Workshop on Machine Learning for Signal Processing en
dc.rights openAccess en
dc.subject.other 217 Medical engineering en
dc.subject.other 213 Electronic, automation and communications engineering, electronics en
dc.title Spectro-Temporal ECG Analysis for Atrial Fibrillation Detection en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.subject.keyword atrial fibrillation
dc.subject.keyword deep learning
dc.subject.keyword Kalman filter
dc.subject.keyword state-space model
dc.subject.keyword spectrogram estimation
dc.subject.keyword 217 Medical engineering
dc.subject.keyword 213 Electronic, automation and communications engineering, electronics
dc.identifier.urn URN:NBN:fi:aalto-201901141103
dc.identifier.doi 10.1109/MLSP.2018.8517085
dc.type.version acceptedVersion


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