Heart Rate Turbulence Detection Using Mean Shape Information

Danny Smith; Kristian Solem; P. Laguna; J. P. Martinez; Leif Sörnmo

Heart Rate Turbulence Detection Using Mean Shape Information

Danny Smith, Kristian Solem, P. Laguna, J. P. Martinez, Leif Sörnmo

Institutionen för elektro- och informationsteknik

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

In this study, we propose a generalized likelihood ratio test statistic for detection of heart rate turbulence (HRT) based on a linear signal model. The new test statistic, which expands our previous original detector; takes a priori information regarding HRT shape into account. The detector structure is based on the extended integral pulse frequency modulation model which accounts for the presence of ectopic beats and HRT The spectral relationship between heart rate variability (HRV) and HRT is investigated for the purpose of modeling HRV "noise" present during the turbulence period. The performance was studied for both simulated data and real data obtained from the Long-Term ST database. The results show that the new detector is superior to the original one as well as to the commonly used parameter turbulence slope (TS) on both types of data.

Originalspråk	engelska
Titel på värdpublikation	CINC: 2009 36th Annual Computers in Cardiology Conference
Förlag	IEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor	153-156
Status	Published - 2009
Evenemang	36th Annual Computers in Cardiology Conference, 2009 - Pk City, UT, Park City, UT, USA Varaktighet: 2009 sep. 13 → 2009 sep. 16 Konferensnummer: 36

Publikationsserier

Namn
ISSN (tryckt)	0276-6574

Konferens

Konferens	36th Annual Computers in Cardiology Conference, 2009
Förkortad titel	CinC 2009
Land/Territorium	USA
Ort	Park City, UT
Period	2009/09/13 → 2009/09/16

Ämnesklassifikation (UKÄ)

Elektroteknik och elektronik

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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5445446

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@inproceedings{a75fcbec81a844d095f7af9dd0f83771,

title = "Heart Rate Turbulence Detection Using Mean Shape Information",

abstract = "In this study, we propose a generalized likelihood ratio test statistic for detection of heart rate turbulence (HRT) based on a linear signal model. The new test statistic, which expands our previous original detector; takes a priori information regarding HRT shape into account. The detector structure is based on the extended integral pulse frequency modulation model which accounts for the presence of ectopic beats and HRT The spectral relationship between heart rate variability (HRV) and HRT is investigated for the purpose of modeling HRV {"}noise{"} present during the turbulence period. The performance was studied for both simulated data and real data obtained from the Long-Term ST database. The results show that the new detector is superior to the original one as well as to the commonly used parameter turbulence slope (TS) on both types of data.",

author = "Danny Smith and Kristian Solem and P. Laguna and Martinez, {J. P.} and Leif S{\"o}rnmo",

year = "2009",

language = "English",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

pages = "153--156",

booktitle = "CINC: 2009 36th Annual Computers in Cardiology Conference",

address = "United States",

note = "36th Annual Computers in Cardiology Conference, 2009, CinC 2009 ; Conference date: 13-09-2009 Through 16-09-2009",

}

Smith, D, Solem, K, Laguna, P, Martinez, JP & Sörnmo, L 2009, Heart Rate Turbulence Detection Using Mean Shape Information. i CINC: 2009 36th Annual Computers in Cardiology Conference. IEEE - Institute of Electrical and Electronics Engineers Inc., s. 153-156, 36th Annual Computers in Cardiology Conference, 2009, Park City, UT, USA, 2009/09/13. <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5445446>

TY - GEN

T1 - Heart Rate Turbulence Detection Using Mean Shape Information

AU - Smith, Danny

AU - Solem, Kristian

AU - Laguna, P.

AU - Martinez, J. P.

AU - Sörnmo, Leif

N1 - Conference code: 36

PY - 2009

Y1 - 2009

N2 - In this study, we propose a generalized likelihood ratio test statistic for detection of heart rate turbulence (HRT) based on a linear signal model. The new test statistic, which expands our previous original detector; takes a priori information regarding HRT shape into account. The detector structure is based on the extended integral pulse frequency modulation model which accounts for the presence of ectopic beats and HRT The spectral relationship between heart rate variability (HRV) and HRT is investigated for the purpose of modeling HRV "noise" present during the turbulence period. The performance was studied for both simulated data and real data obtained from the Long-Term ST database. The results show that the new detector is superior to the original one as well as to the commonly used parameter turbulence slope (TS) on both types of data.

AB - In this study, we propose a generalized likelihood ratio test statistic for detection of heart rate turbulence (HRT) based on a linear signal model. The new test statistic, which expands our previous original detector; takes a priori information regarding HRT shape into account. The detector structure is based on the extended integral pulse frequency modulation model which accounts for the presence of ectopic beats and HRT The spectral relationship between heart rate variability (HRV) and HRT is investigated for the purpose of modeling HRV "noise" present during the turbulence period. The performance was studied for both simulated data and real data obtained from the Long-Term ST database. The results show that the new detector is superior to the original one as well as to the commonly used parameter turbulence slope (TS) on both types of data.

M3 - Paper in conference proceeding

SP - 153

EP - 156

BT - CINC: 2009 36th Annual Computers in Cardiology Conference

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th Annual Computers in Cardiology Conference, 2009

Y2 - 13 September 2009 through 16 September 2009

ER -

Heart Rate Turbulence Detection Using Mean Shape Information

Sammanfattning

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