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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doaj-2b9fce1c1b9f4f0097f4d9a8792ee3a42020-12-08T06:55:10ZengNature Publishing GroupScientific Reports2045-23222019-10-019111810.1038/s41598-019-51061-8Phase Space Reconstruction Based CVD Classifier Using Localized FeaturesNaresh Vemishetty0Ramya Lakshmi Gunukula1Amit Acharyya2Paolo Emilio Puddu3Saptarshi Das4Koushik Maharatna5Department of Electrical Engineering, IIT HyderabadDepartment of Electrical Engineering, IIT HyderabadDepartment of Electrical Engineering, IIT HyderabadDepartment of Cardiovascular Sciences, Sapienza University of RomeDepartment of Mathematics, University of ExeterSchool of Electronics and Computer Science, University of SouthamptonAbstract 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.https://doi.org/10.1038/s41598-019-51061-8 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Naresh Vemishetty Ramya Lakshmi Gunukula Amit Acharyya Paolo Emilio Puddu Saptarshi Das Koushik Maharatna |
spellingShingle |
Naresh Vemishetty Ramya Lakshmi Gunukula Amit Acharyya Paolo Emilio Puddu Saptarshi Das Koushik Maharatna Phase Space Reconstruction Based CVD Classifier Using Localized Features Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2019-10-01 |
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. |
url |
https://doi.org/10.1038/s41598-019-51061-8 |
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