Wavelet transform as signal processing method for ANN-classification of acute coronary syndrome
(2011) FYTK01 20111Computational Biology and Biological Physics - Has been reorganised
- Abstract
- When diagnosing acute coronary syndrome, time is of the essence and electrocardiography the most effective method to obtain data about the patients condition. Artificial neural networks can here be used to assist medical doctors.
In this text the wavelet transform is introduced as a signal processing method for piping ECG-curves to ANN’s. The performance of this setup (ANN performance is measured by AUC, the area under the receiver operating characteristic curve) varies around 0.60-0.65, significantly worse than that of previous methods, with an AUC of over 0.80. Some insight into ECG-curve properties and the compressional values of wavelet transforms is gained however.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/2204713
- author
- Åström, Hampus LU
- supervisor
- organization
- course
- FYTK01 20111
- year
- 2011
- type
- M2 - Bachelor Degree
- subject
- keywords
- wavelet transform, artificial neural networks, acute coronary syndrome, medical decision support
- language
- English
- id
- 2204713
- date added to LUP
- 2011-11-21 08:49:10
- date last changed
- 2017-10-06 16:47:25
@misc{2204713, abstract = {{When diagnosing acute coronary syndrome, time is of the essence and electrocardiography the most effective method to obtain data about the patients condition. Artificial neural networks can here be used to assist medical doctors. In this text the wavelet transform is introduced as a signal processing method for piping ECG-curves to ANN’s. The performance of this setup (ANN performance is measured by AUC, the area under the receiver operating characteristic curve) varies around 0.60-0.65, significantly worse than that of previous methods, with an AUC of over 0.80. Some insight into ECG-curve properties and the compressional values of wavelet transforms is gained however.}}, author = {{Åström, Hampus}}, language = {{eng}}, note = {{Student Paper}}, title = {{Wavelet transform as signal processing method for ANN-classification of acute coronary syndrome}}, year = {{2011}}, }