| Full text | |
| Author(s): |
Hadjiloucas, S.
;
Jannah, N.
;
Hwang, F.
;
Galvao, R. K. H.
;
Vagenas, EC
;
Vlachos, DS
Total Authors: 6
|
| Document type: | Journal article |
| Source: | WAKE CONFERENCE 2021; v. 490, p. 5-pg., 2014-01-01. |
| Abstract | |
This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier. (AU) | |
| FAPESP's process: | 11/17610-0 - Monitoring and control of dynamic systems subject to faults |
| Grantee: | Roberto Kawakami Harrop Galvão |
| Support Opportunities: | Research Projects - Thematic Grants |