The University of Southampton
University of Southampton Institutional Repository

Phase space reconstruction based CVD classifier using localized features

Phase space reconstruction based CVD classifier using localized features
Phase space reconstruction based CVD classifier using localized features
This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.
phase space reconstruction, Arrhythmia Prediction, electrocardiogram, machine learning, Biomedical signal processing
2045-2322
1-18
Vemishetty, Naresh
71fa9f69-5971-462a-82b4-fe5b4b77abae
Gunukula, Ramya Laxmi
32d76f5f-a7b9-4f45-bbe2-c12c80d1aa8b
Acharyya, Amit
f89301cf-c92e-4fa0-9148-6486dd088034
Puddu, Paolo Emilio
bc5a0033-3983-4879-8c99-cb480b4c79bb
Das, Saptarshi
4abee5b7-7af2-40c4-aee3-affbb99f4d69
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Vemishetty, Naresh
71fa9f69-5971-462a-82b4-fe5b4b77abae
Gunukula, Ramya Laxmi
32d76f5f-a7b9-4f45-bbe2-c12c80d1aa8b
Acharyya, Amit
f89301cf-c92e-4fa0-9148-6486dd088034
Puddu, Paolo Emilio
bc5a0033-3983-4879-8c99-cb480b4c79bb
Das, Saptarshi
4abee5b7-7af2-40c4-aee3-affbb99f4d69
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Vemishetty, Naresh, Gunukula, Ramya Laxmi, Acharyya, Amit, Puddu, Paolo Emilio, Das, Saptarshi and Maharatna, Koushik (2019) Phase space reconstruction based CVD classifier using localized features. Scientific Reports, 9, 1-18, [14593]. (doi:10.1038/s41598-019-51061-8).

Record type: Article

Abstract

This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.

Text
phase_space_nature - Version of Record
Available under License Creative Commons Attribution.
Download (3MB)
Text
s41598-019-51061-8 - Version of Record
Available under License Creative Commons Attribution.
Download (3MB)

More information

Accepted/In Press date: 23 August 2019
e-pub ahead of print date: 10 October 2019
Keywords: phase space reconstruction, Arrhythmia Prediction, electrocardiogram, machine learning, Biomedical signal processing

Identifiers

Local EPrints ID: 435045
URI: http://eprints.soton.ac.uk/id/eprint/435045
ISSN: 2045-2322
PURE UUID: 5db8593a-20fe-48c7-87d7-a97f042c8533

Catalogue record

Date deposited: 21 Oct 2019 16:30
Last modified: 06 Oct 2020 19:19

Export record

Altmetrics

Contributors

Author: Naresh Vemishetty
Author: Ramya Laxmi Gunukula
Author: Amit Acharyya
Author: Paolo Emilio Puddu
Author: Saptarshi Das
Author: Koushik Maharatna

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×