On-line Bayesian parameter estimation in electrocardiogram state space models

Kimmo Suotsalo; Simo Särkkä

doi:10.1109/MLSP.2018.8517028

On-line Bayesian parameter estimation in electrocardiogram state space models

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

This paper proposes a Bayesian approach to parameter estimation in electrocardiogram state space models. The on-line nature of the proposed method allows it to be applied to real-world electrocardiogram recordings with varying beat morphology, heart rate, and noise; it thereby provides clear advantages over the conventional Gaussian kernel approach. The applicability of the proposed method is demonstrated on benchmark electrocardiogram data. The results indicate that the method provides a promising framework for noise reduction and wave delineation in electrocardiograms.

Original language	English
Title of host publication	2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
Editors	Nelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Publisher	IEEE
Volume	2018-September
ISBN (Electronic)	9781538654774
DOIs	https://doi.org/10.1109/MLSP.2018.8517028
Publication status	Published - 31 Oct 2018
MoE publication type	A4 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
Publisher	IEEE
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Abbreviated title	MLSP
Country/Territory	Denmark
City	Aalborg
Period	17/09/2018 → 20/09/2018

Keywords

Bayesian filtering
Electrocardiography
Parameter estimation
State space model
Wave delineation

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10.1109/MLSP.2018.8517028

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Suotsalo, K., & Särkkä, S. (2018). On-line Bayesian parameter estimation in electrocardiogram state space models. In N. Pustelnik, Z-H. Tan, Z. Ma, & J. Larsen (Eds.), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings (Vol. 2018-September). Article 8517028 (IEEE International Workshop on Machine Learning for Signal Processing). IEEE. https://doi.org/10.1109/MLSP.2018.8517028

Suotsalo, Kimmo ; Särkkä, Simo. / On-line Bayesian parameter estimation in electrocardiogram state space models. 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. editor / Nelly Pustelnik ; Zheng-Hua Tan ; Zhanyu Ma ; Jan Larsen. Vol. 2018-September IEEE, 2018. (IEEE International Workshop on Machine Learning for Signal Processing).

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abstract = "This paper proposes a Bayesian approach to parameter estimation in electrocardiogram state space models. The on-line nature of the proposed method allows it to be applied to real-world electrocardiogram recordings with varying beat morphology, heart rate, and noise; it thereby provides clear advantages over the conventional Gaussian kernel approach. The applicability of the proposed method is demonstrated on benchmark electrocardiogram data. The results indicate that the method provides a promising framework for noise reduction and wave delineation in electrocardiograms.",

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doi = "10.1109/MLSP.2018.8517028",

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Suotsalo, K & Särkkä, S 2018, On-line Bayesian parameter estimation in electrocardiogram state space models. in N Pustelnik, Z-H Tan, Z Ma & J Larsen (eds), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. vol. 2018-September, 8517028, 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.2018.8517028

On-line Bayesian parameter estimation in electrocardiogram state space models. / Suotsalo, Kimmo; Särkkä, Simo.
2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. ed. / Nelly Pustelnik; Zheng-Hua Tan; Zhanyu Ma; Jan Larsen. Vol. 2018-September IEEE, 2018. 8517028 (IEEE International Workshop on Machine Learning for Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Särkkä, Simo

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PY - 2018/10/31

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N2 - This paper proposes a Bayesian approach to parameter estimation in electrocardiogram state space models. The on-line nature of the proposed method allows it to be applied to real-world electrocardiogram recordings with varying beat morphology, heart rate, and noise; it thereby provides clear advantages over the conventional Gaussian kernel approach. The applicability of the proposed method is demonstrated on benchmark electrocardiogram data. The results indicate that the method provides a promising framework for noise reduction and wave delineation in electrocardiograms.

AB - This paper proposes a Bayesian approach to parameter estimation in electrocardiogram state space models. The on-line nature of the proposed method allows it to be applied to real-world electrocardiogram recordings with varying beat morphology, heart rate, and noise; it thereby provides clear advantages over the conventional Gaussian kernel approach. The applicability of the proposed method is demonstrated on benchmark electrocardiogram data. The results indicate that the method provides a promising framework for noise reduction and wave delineation in electrocardiograms.

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KW - Wave delineation

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T3 - IEEE International Workshop on Machine Learning for Signal Processing

BT - 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings

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Suotsalo K, Särkkä S. On-line Bayesian parameter estimation in electrocardiogram state space models. In Pustelnik N, Tan Z-H, Ma Z, Larsen J, editors, 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. Vol. 2018-September. IEEE. 2018. 8517028. (IEEE International Workshop on Machine Learning for Signal Processing). doi: 10.1109/MLSP.2018.8517028

On-line Bayesian parameter estimation in electrocardiogram state space models

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