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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
Toimittajat	Nelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Kustantaja	IEEE
Vuosikerta	2018-September
ISBN (elektroninen)	9781538654774
DOI - pysyväislinkit	https://doi.org/10.1109/MLSP.2018.8517028
Tila	Julkaistu - 31 lokak. 2018
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Machine Learning for Signal Processing - Aalborg, Tanska Kesto: 17 syysk. 2018 → 20 syysk. 2018 Konferenssinumero: 28

Julkaisusarja

Nimi	IEEE International Workshop on Machine Learning for Signal Processing
Kustantaja	IEEE
ISSN (painettu)	2161-0363
ISSN (elektroninen)	2161-0371

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Lyhennettä	MLSP
Maa/Alue	Tanska
Kaupunki	Aalborg
Ajanjakso	17/09/2018 → 20/09/2018

Pääsy asiakirjaan

10.1109/MLSP.2018.8517028

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Link to publication in Scopus

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Suotsalo, K., & Särkkä, S. (2018). On-line Bayesian parameter estimation in electrocardiogram state space models. teoksessa N. Pustelnik, Z.-H. Tan, Z. Ma, & J. Larsen (Toimittajat), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings (Vuosikerta 2018-September). Artikkeli 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. Toimittaja / Nelly Pustelnik ; Zheng-Hua Tan ; Zhanyu Ma ; Jan Larsen. Vuosikerta 2018-September IEEE, 2018. (IEEE International Workshop on Machine Learning for Signal Processing).

@inproceedings{18e0355fc6ca446bb2fef8e5cdac620f,

title = "On-line Bayesian parameter estimation in electrocardiogram state space models",

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.",

keywords = "Bayesian filtering, Electrocardiography, Parameter estimation, State space model, Wave delineation",

author = "Kimmo Suotsalo and Simo S{\"a}rkk{\"a}",

year = "2018",

month = oct,

day = "31",

doi = "10.1109/MLSP.2018.8517028",

language = "English",

volume = "2018-September",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "IEEE",

editor = "Nelly Pustelnik and Zheng-Hua Tan and Zhanyu Ma and Jan Larsen",

booktitle = "2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings",

address = "United States",

note = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP ; Conference date: 17-09-2018 Through 20-09-2018",

}

Suotsalo, K & Särkkä, S 2018, On-line Bayesian parameter estimation in electrocardiogram state space models. julkaisussa N Pustelnik, Z-H Tan, Z Ma & J Larsen (toim), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. Vuosikerta 2018-September, 8517028, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing, Aalborg, Tanska, 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. toim. / Nelly Pustelnik; Zheng-Hua Tan; Zhanyu Ma; Jan Larsen. Vuosikerta 2018-September IEEE, 2018. 8517028 (IEEE International Workshop on Machine Learning for Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - On-line Bayesian parameter estimation in electrocardiogram state space models

AU - Suotsalo, Kimmo

AU - Särkkä, Simo

N1 - Conference code: 28

PY - 2018/10/31

Y1 - 2018/10/31

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.

KW - Bayesian filtering

KW - Electrocardiography

KW - Parameter estimation

KW - State space model

KW - Wave delineation

UR - https://www.scopus.com/pages/publications/85056998348

U2 - 10.1109/MLSP.2018.8517028

DO - 10.1109/MLSP.2018.8517028

M3 - Conference article in proceedings

AN - SCOPUS:85056998348

VL - 2018-September

T3 - IEEE International Workshop on Machine Learning for Signal Processing

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

A2 - Pustelnik, Nelly

A2 - Tan, Zheng-Hua

A2 - Ma, Zhanyu

A2 - Larsen, Jan

PB - IEEE

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 17 September 2018 through 20 September 2018

ER -

Suotsalo K, Särkkä S. On-line Bayesian parameter estimation in electrocardiogram state space models. julkaisussa Pustelnik N, Tan ZH, Ma Z, Larsen J, toimittajat, 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings. Vuosikerta 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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