Phase Space Reconstruction Based CVD Classifier Using Localized Features

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 in...

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Autores principales:	Naresh Vemishetty, Ramya Lakshmi Gunukula, Amit Acharyya, Paolo Emilio Puddu, Saptarshi Das, Koushik Maharatna
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b9fce1c1b9f4f0097f4d9a8792ee3a4
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spelling	oai:doaj.org-article:2b9fce1c1b9f4f0097f4d9a8792ee3a42021-12-02T15:08:08ZPhase Space Reconstruction Based CVD Classifier Using Localized Features10.1038/s41598-019-51061-82045-2322https://doaj.org/article/2b9fce1c1b9f4f0097f4d9a8792ee3a42019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-51061-8https://doaj.org/toc/2045-2322Abstract 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.Naresh VemishettyRamya Lakshmi GunukulaAmit AcharyyaPaolo Emilio PudduSaptarshi DasKoushik MaharatnaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-18 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naresh Vemishetty Ramya Lakshmi Gunukula Amit Acharyya Paolo Emilio Puddu Saptarshi Das Koushik Maharatna Phase Space Reconstruction Based CVD Classifier Using Localized Features
description	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.
format	article
author	Naresh Vemishetty Ramya Lakshmi Gunukula Amit Acharyya Paolo Emilio Puddu Saptarshi Das Koushik Maharatna
author_facet	Naresh Vemishetty Ramya Lakshmi Gunukula Amit Acharyya Paolo Emilio Puddu Saptarshi Das Koushik Maharatna
author_sort	Naresh Vemishetty
title	Phase Space Reconstruction Based CVD Classifier Using Localized Features
title_short	Phase Space Reconstruction Based CVD Classifier Using Localized Features
title_full	Phase Space Reconstruction Based CVD Classifier Using Localized Features
title_fullStr	Phase Space Reconstruction Based CVD Classifier Using Localized Features
title_full_unstemmed	Phase Space Reconstruction Based CVD Classifier Using Localized Features
title_sort	phase space reconstruction based cvd classifier using localized features
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/2b9fce1c1b9f4f0097f4d9a8792ee3a4
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Phase Space Reconstruction Based CVD Classifier Using Localized Features

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