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 contribution › 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 konferenssijulkaisuussa
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

OpenUrl-saatavuus

Koko teksti

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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). [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 contribution › 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 - http://www.scopus.com/inward/record.url?scp=85056998348&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2018.8517028

DO - 10.1109/MLSP.2018.8517028

M3 - Conference contribution

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 Z-H, 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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