Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

Rasmus S.  Andersen; Erik S.  Poulsen; Sadasivan Puthusserypady

doi:10.1109/EMBC.2017.8037253

Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

Rasmus S. Andersen
, Erik S. Poulsen
, Sadasivan Puthusserypady

Copenhagen Center for Health Technology

Technical University of Denmark
Cortrium ApS

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

Abstract

Atrial ﬁbrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efﬁciently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classiﬁcation. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classiﬁcation performance (Sensitivity≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for signiﬁcantly reduced computation time without loss of performance.

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	2039-2042
ISBN (Print)	978-1-5090-2809-2
DOIs	https://doi.org/10.1109/EMBC.2017.8037253
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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@inproceedings{7c3da38cde434b569d33f12ef0bc5882,

title = "Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine",

abstract = "Atrial ﬁbrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efﬁciently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classiﬁcation. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classiﬁcation performance (Sensitivity≥ 95\%). The proposed approach requires detection of R-peaks in the ECG signal but allows for signiﬁcantly reduced computation time without loss of performance.",

author = "Andersen, \{Rasmus S.\} and Poulsen, \{Erik S.\} and Sadasivan Puthusserypady",

year = "2017",

doi = "10.1109/EMBC.2017.8037253",

language = "English",

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

pages = "2039--2042",

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",

}

Andersen, RS, Poulsen, ES & Puthusserypady, S 2017, Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine. in Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 2039-2042, 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.2017.8037253

Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine. / Andersen, Rasmus S. ; Poulsen, Erik S. ; Puthusserypady, Sadasivan.
Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2017. p. 2039-2042.

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

TY - GEN

T1 - Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

AU - Andersen, Rasmus S.

AU - Poulsen, Erik S.

AU - Puthusserypady, Sadasivan

N1 - Conference code: 39

PY - 2017

Y1 - 2017

N2 - Atrial ﬁbrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efﬁciently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classiﬁcation. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classiﬁcation performance (Sensitivity≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for signiﬁcantly reduced computation time without loss of performance.

AB - Atrial ﬁbrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efﬁciently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classiﬁcation. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classiﬁcation performance (Sensitivity≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for signiﬁcantly reduced computation time without loss of performance.

U2 - 10.1109/EMBC.2017.8037253

DO - 10.1109/EMBC.2017.8037253

M3 - Article in proceedings

C2 - 29060297

SN - 978-1-5090-2809-2

SP - 2039

EP - 2042

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

PB - IEEE

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

Y2 - 11 July 2017 through 15 July 2017

ER -

Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

Abstract

Conference

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