Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators
Abstract Sudden cardiac arrest (SCA) consisting of ventricular fibrillation and ventricular tachycardia considered as shockable rhythms is a life-threatening heart disease, which is treated efficiently by the automated external defibrillator (AED). This work proposes a novel design of the SAA, which...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-36011-9 |
| _version_ | 1797811530636460032 |
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| author | Van-Su Pham Anh Nguyen Hoai Bac Dang Hai-Chau Le Minh Tuan Nguyen |
| author_facet | Van-Su Pham Anh Nguyen Hoai Bac Dang Hai-Chau Le Minh Tuan Nguyen |
| author_sort | Van-Su Pham |
| collection | DOAJ |
| description | Abstract Sudden cardiac arrest (SCA) consisting of ventricular fibrillation and ventricular tachycardia considered as shockable rhythms is a life-threatening heart disease, which is treated efficiently by the automated external defibrillator (AED). This work proposes a novel design of the SAA, which includes a k-nearest neighbors model and a subset of 8 features extracted from the ECG segments, for the SCA diagnosis on the electrocardiogram (ECG) signal. These features are addressed as the most productive subset among 31 input features based on the evaluation of the feature correlation. The recursive feature elimination algorithm combined with the Boosting model and wise-patient fivefold cross-validation method is adopted for the calculation of the average feature importance, which shows the degree of feature correlation, to construct various input feature subsets. Moreover, component feature combinations known as the representatives of the input feature subsets with an enormous level of correlation and independence are transformed from the input subsets by the principal component analysis method. The wise-patient fivefold cross-validation procedure is used for the evaluation of these component feature combinations on the validation set. The proposed SAA is certainly efficient for SCA detection with a small number of the extracted feature and relatively high diagnosis performance such as accuracy of 99.52%, sensitivity of 97.69%, and specificity of 99.91%. |
| first_indexed | 2024-03-13T07:25:05Z |
| format | Article |
| id | doaj.art-29db1117dd504283a2cdcd711ddcbab8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T07:25:05Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-29db1117dd504283a2cdcd711ddcbab82023-06-04T11:25:27ZengNature PortfolioScientific Reports2045-23222023-05-0113111010.1038/s41598-023-36011-9Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillatorsVan-Su Pham0Anh Nguyen1Hoai Bac Dang2Hai-Chau Le3Minh Tuan Nguyen4Data and intelligent systems laboratory, Posts and Telecommunications Institute of TechnologyData and intelligent systems laboratory, Posts and Telecommunications Institute of TechnologyPosts and Telecommunications Institute of TechnologyData and intelligent systems laboratory, Posts and Telecommunications Institute of TechnologyData and intelligent systems laboratory, Posts and Telecommunications Institute of TechnologyAbstract Sudden cardiac arrest (SCA) consisting of ventricular fibrillation and ventricular tachycardia considered as shockable rhythms is a life-threatening heart disease, which is treated efficiently by the automated external defibrillator (AED). This work proposes a novel design of the SAA, which includes a k-nearest neighbors model and a subset of 8 features extracted from the ECG segments, for the SCA diagnosis on the electrocardiogram (ECG) signal. These features are addressed as the most productive subset among 31 input features based on the evaluation of the feature correlation. The recursive feature elimination algorithm combined with the Boosting model and wise-patient fivefold cross-validation method is adopted for the calculation of the average feature importance, which shows the degree of feature correlation, to construct various input feature subsets. Moreover, component feature combinations known as the representatives of the input feature subsets with an enormous level of correlation and independence are transformed from the input subsets by the principal component analysis method. The wise-patient fivefold cross-validation procedure is used for the evaluation of these component feature combinations on the validation set. The proposed SAA is certainly efficient for SCA detection with a small number of the extracted feature and relatively high diagnosis performance such as accuracy of 99.52%, sensitivity of 97.69%, and specificity of 99.91%.https://doi.org/10.1038/s41598-023-36011-9 |
| spellingShingle | Van-Su Pham Anh Nguyen Hoai Bac Dang Hai-Chau Le Minh Tuan Nguyen Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators Scientific Reports |
| title | Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators |
| title_full | Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators |
| title_fullStr | Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators |
| title_full_unstemmed | Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators |
| title_short | Diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators |
| title_sort | diagnosis of sudden cardiac arrest using principal component analysis in automated external defibrillators |
| url | https://doi.org/10.1038/s41598-023-36011-9 |
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