Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis

Md Asif Khan; Kazi Asfaq Ahmed Ador; Tanzilur Rahman

doi:10.1109/TENCON.2019.8929421

Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis

Md Asif Khan, Kazi Asfaq Ahmed Ador, Tanzilur Rahman

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

Abstract

Atrial Fibrillation (AF) is quivering or irregular heartbeat by which the two upper chambers of the heart (atria) get affected resulting in disruption of blood flow throughout the body. If left untreated, AF can lead to several heart-related complications. In this study, we propose a signal processing model to automatically detect AF from electrocardiogram (ECG) signal at a shorter duration (9 s) that will be useful for server-based applications, remote monitoring and automated detection of AF. The ECG data collected from 'CPSC-18 Challenge' have been preprocessed with FIR filter followed by the extraction of important features. RR interval (RRI) and RRI combined with mean RS ratio have particularly been found to be useful in detecting AF. Principal Component Analysis-Discriminant Analysis (PCA-DA) along with two other popular classifiers have been applied on the extracted features. The PCA-DA based approach had been superior in the detection of AF giving an accuracy of 97% with sensitivity and specificity both being 0.97. The proposed approach can be beneficial for noninvasive and faster screening of AF.

Original language	English
Title of host publication	Proceedings of the TENCON 2019
Subtitle of host publication	Technology, Knowledge, and Society
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2289-2294
Number of pages	6
ISBN (Electronic)	9781728118956
DOIs	https://doi.org/10.1109/TENCON.2019.8929421
State	Published - Oct 2019
Externally published	Yes
Event	2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 - Kerala, India Duration: 17 Oct 2019 → 20 Oct 2019

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2019-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019
Country/Territory	India
City	Kerala
Period	17/10/19 → 20/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Atrial Fibrillation Detection
Biomedical Signal Processing
Machine Learning
PCA-DA
Time domain features

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2019.8929421

Cite this

Khan, M. A., Ador, K. A. A., & Rahman, T. (2019). Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society (pp. 2289-2294). Article 8929421 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2019.8929421

Khan, Md Asif ; Ador, Kazi Asfaq Ahmed ; Rahman, Tanzilur. / Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis. Proceedings of the TENCON 2019: Technology, Knowledge, and Society. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2289-2294 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

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title = "Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis",

abstract = "Atrial Fibrillation (AF) is quivering or irregular heartbeat by which the two upper chambers of the heart (atria) get affected resulting in disruption of blood flow throughout the body. If left untreated, AF can lead to several heart-related complications. In this study, we propose a signal processing model to automatically detect AF from electrocardiogram (ECG) signal at a shorter duration (9 s) that will be useful for server-based applications, remote monitoring and automated detection of AF. The ECG data collected from 'CPSC-18 Challenge' have been preprocessed with FIR filter followed by the extraction of important features. RR interval (RRI) and RRI combined with mean RS ratio have particularly been found to be useful in detecting AF. Principal Component Analysis-Discriminant Analysis (PCA-DA) along with two other popular classifiers have been applied on the extracted features. The PCA-DA based approach had been superior in the detection of AF giving an accuracy of 97\% with sensitivity and specificity both being 0.97. The proposed approach can be beneficial for noninvasive and faster screening of AF.",

keywords = "Atrial Fibrillation Detection, Biomedical Signal Processing, Machine Learning, PCA-DA, Time domain features",

author = "Khan, \{Md Asif\} and Ador, \{Kazi Asfaq Ahmed\} and Tanzilur Rahman",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 ; Conference date: 17-10-2019 Through 20-10-2019",

year = "2019",

month = oct,

doi = "10.1109/TENCON.2019.8929421",

language = "English",

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Khan, MA, Ador, KAA & Rahman, T 2019, Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis. in Proceedings of the TENCON 2019: Technology, Knowledge, and Society., 8929421, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 2289-2294, 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019, Kerala, India, 17/10/19. https://doi.org/10.1109/TENCON.2019.8929421

Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis. / Khan, Md Asif; Ador, Kazi Asfaq Ahmed; Rahman, Tanzilur.
Proceedings of the TENCON 2019: Technology, Knowledge, and Society. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2289-2294 8929421 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2019-October).

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

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AU - Ador, Kazi Asfaq Ahmed

AU - Rahman, Tanzilur

PY - 2019/10

Y1 - 2019/10

N2 - Atrial Fibrillation (AF) is quivering or irregular heartbeat by which the two upper chambers of the heart (atria) get affected resulting in disruption of blood flow throughout the body. If left untreated, AF can lead to several heart-related complications. In this study, we propose a signal processing model to automatically detect AF from electrocardiogram (ECG) signal at a shorter duration (9 s) that will be useful for server-based applications, remote monitoring and automated detection of AF. The ECG data collected from 'CPSC-18 Challenge' have been preprocessed with FIR filter followed by the extraction of important features. RR interval (RRI) and RRI combined with mean RS ratio have particularly been found to be useful in detecting AF. Principal Component Analysis-Discriminant Analysis (PCA-DA) along with two other popular classifiers have been applied on the extracted features. The PCA-DA based approach had been superior in the detection of AF giving an accuracy of 97% with sensitivity and specificity both being 0.97. The proposed approach can be beneficial for noninvasive and faster screening of AF.

AB - Atrial Fibrillation (AF) is quivering or irregular heartbeat by which the two upper chambers of the heart (atria) get affected resulting in disruption of blood flow throughout the body. If left untreated, AF can lead to several heart-related complications. In this study, we propose a signal processing model to automatically detect AF from electrocardiogram (ECG) signal at a shorter duration (9 s) that will be useful for server-based applications, remote monitoring and automated detection of AF. The ECG data collected from 'CPSC-18 Challenge' have been preprocessed with FIR filter followed by the extraction of important features. RR interval (RRI) and RRI combined with mean RS ratio have particularly been found to be useful in detecting AF. Principal Component Analysis-Discriminant Analysis (PCA-DA) along with two other popular classifiers have been applied on the extracted features. The PCA-DA based approach had been superior in the detection of AF giving an accuracy of 97% with sensitivity and specificity both being 0.97. The proposed approach can be beneficial for noninvasive and faster screening of AF.

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KW - Biomedical Signal Processing

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 17 October 2019 through 20 October 2019

ER -

Khan MA, Ador KAA, Rahman T. Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2289-2294. 8929421. (IEEE Region 10 Annual International Conference, Proceedings/TENCON). doi: 10.1109/TENCON.2019.8929421

Automated Diagnosis of Atrial Fibrillation Using Principal Component Analysis-Discriminant Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this