Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit

Iben H.  Bruun; Semira M. S.  Hissabu; Erik S. Poulsen; Sadasivan Puthusserypady

doi:10.1109/EMBC.2017.8037728

Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit

Iben H. Bruun
, Semira M. S. Hissabu
, Erik S. Poulsen
, Sadasivan Puthusserypady

Technical University of Denmark
Cortrium ApS

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classiﬁcation. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a speciﬁcity of 99.19%, and an overall accuracy of 98.22%

Original language	English
Title of host publication	Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher	IEEE
Publication date	2017
Pages	3981-3984
ISBN (Print)	978-1-5090-2809-2/
DOIs	https://doi.org/10.1109/EMBC.2017.8037728
Publication status	Published - 2017
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - International Convention Center, Jeju Island, Jeju, Korea, Republic of Duration: 11 Jul 2017 → 15 Jul 2017 Conference number: 39

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number	39
Location	International Convention Center, Jeju Island
Country/Territory	Korea, Republic of
City	Jeju
Period	11/07/2017 → 15/07/2017

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Bruun, I. H., Hissabu, S. M. S., Poulsen, E. S., & Puthusserypady, S. (2017). Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit. In Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3981-3984). IEEE. https://doi.org/10.1109/EMBC.2017.8037728

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title = "Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit",

abstract = "Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classiﬁcation. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51\%, a speciﬁcity of 99.19\%, and an overall accuracy of 98.22\%",

author = "Bruun, \{Iben H.\} and Hissabu, \{Semira M. S.\} and Poulsen, \{Erik S.\} and Sadasivan Puthusserypady",

year = "2017",

doi = "10.1109/EMBC.2017.8037728",

language = "English",

isbn = "978-1-5090-2809-2/",

pages = "3981--3984",

booktitle = "Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",

publisher = "IEEE",

address = "United States",

note = "39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",

}

Bruun, IH, Hissabu, SMS, Poulsen, ES & Puthusserypady, S 2017, Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit. in Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 3981-3984, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, Korea, Republic of, 11/07/2017. https://doi.org/10.1109/EMBC.2017.8037728

Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit. / Bruun, Iben H. ; Hissabu, Semira M. S. ; Poulsen, Erik S. et al.
Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2017. p. 3981-3984.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit

AU - Bruun, Iben H.

AU - Hissabu, Semira M. S.

AU - Poulsen, Erik S.

AU - Puthusserypady, Sadasivan

N1 - Conference code: 39

PY - 2017

Y1 - 2017

N2 - Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classiﬁcation. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a speciﬁcity of 99.19%, and an overall accuracy of 98.22%

AB - Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classiﬁcation. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a speciﬁcity of 99.19%, and an overall accuracy of 98.22%

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DO - 10.1109/EMBC.2017.8037728

M3 - Article in proceedings

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SN - 978-1-5090-2809-2/

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BT - Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 11 July 2017 through 15 July 2017

ER -

Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit

Abstract

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