Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction
This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter...
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| Format: | Article |
| Language: | Spanish |
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Universitat Politècnica de València
2017-12-01
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| Series: | Revista Iberoamericana de Automática e Informática Industrial RIAI |
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| Online Access: | https://polipapers.upv.es/index.php/RIAI/article/view/8833 |
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| author | Azeddine Mjahad Alfredo Rosado Muñoz Manuel Bataller Mompeán Jose V. Francés Víllora Juan F. Guerrero Martínez |
| author_facet | Azeddine Mjahad Alfredo Rosado Muñoz Manuel Bataller Mompeán Jose V. Francés Víllora Juan F. Guerrero Martínez |
| author_sort | Azeddine Mjahad |
| collection | DOAJ |
| description | This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: ’Normal’ for sinus rhythm, ’VT’ for ventricular tachycardia, ’VF’ for ventricular fibrillation and ’Others’ for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27% sensitivity and 98.22% specificity. In case of VT, 88.31% sensitivity and 98.80% specificity is obtained, 98.14% sensitivity and 96.82% specificity for normal rhythms, and 96.91% sensitivity and 99.06% specificity for other rhythms. Finally, results are compared with other authors. |
| format | Article |
| id | doaj-art-6a20b6964bfe46bcbf8fea7d72c50170 |
| institution | DOAJ |
| issn | 1697-7912 1697-7920 |
| language | Spanish |
| publishDate | 2017-12-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| spelling | doaj-art-6a20b6964bfe46bcbf8fea7d72c501702025-08-20T02:51:06ZspaUniversitat Politècnica de ValènciaRevista Iberoamericana de Automática e Informática Industrial RIAI1697-79121697-79202017-12-0115112413210.4995/riai.2017.88336130Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extractionAzeddine Mjahad0Alfredo Rosado Muñoz1Manuel Bataller Mompeán2Jose V. Francés Víllora3Juan F. Guerrero Martínez4Universitat de ValenciaUniversitat de ValenciaUniversitat de ValenciaUniversitat de ValenciaUniversitat de ValenciaThis work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: ’Normal’ for sinus rhythm, ’VT’ for ventricular tachycardia, ’VF’ for ventricular fibrillation and ’Others’ for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27% sensitivity and 98.22% specificity. In case of VT, 88.31% sensitivity and 98.80% specificity is obtained, 98.14% sensitivity and 96.82% specificity for normal rhythms, and 96.91% sensitivity and 99.06% specificity for other rhythms. Finally, results are compared with other authors.https://polipapers.upv.es/index.php/RIAI/article/view/8833Sistemas biomédicosSeñales ElectrocardiográficasRepresentación tiempo-frecuenciaSeñales no estacionariasAnálisis de imágenesClasificación |
| spellingShingle | Azeddine Mjahad Alfredo Rosado Muñoz Manuel Bataller Mompeán Jose V. Francés Víllora Juan F. Guerrero Martínez Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction Revista Iberoamericana de Automática e Informática Industrial RIAI Sistemas biomédicos Señales Electrocardiográficas Representación tiempo-frecuencia Señales no estacionarias Análisis de imágenes Clasificación |
| title | Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction |
| title_full | Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction |
| title_fullStr | Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction |
| title_full_unstemmed | Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction |
| title_short | Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction |
| title_sort | ventricular fibrillation detection using time frequency and the knn classifier without parameter extraction |
| topic | Sistemas biomédicos Señales Electrocardiográficas Representación tiempo-frecuencia Señales no estacionarias Análisis de imágenes Clasificación |
| url | https://polipapers.upv.es/index.php/RIAI/article/view/8833 |
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