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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | Spanish |
| Published: |
Universitat Politècnica de València
2017-12-01
|
| Series: | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/RIAI/article/view/8833 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1697-7912 1697-7920 |