Principal Component Analysis in ECG Signal Processing
By F. Castells, Pablo Laguna, L. Sörnmo, A. Bollmann, J. Millet-Roig. EURASIP Journal on Advances in Signal Processing, 2007
391 citations recorded by the source corpus.
This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained. Aspects on PCA related to data with temporal and spatial correlations are considered as adaptive estimation of principal components is. Several ECG applications are reviewed where PCA techniques have been successfully employed, including data compression, ST-T segment analysis for the detection of myocardial ischemia and abnormalities in ventricular repolarization, extraction of atrial fibrillatory waves for detailed characterization of atrial fibrillation, and analysis of body surface potential maps.
Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/194ec452c14fc9e76386e0fefb5f7610c9ef07f8
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In Cardiology and Cardiovascular Medicine
- Michele Rossi
- Eduardo José da S. Luz
- Guillermo Cámara Chávez
- Davide Del Testa
- David Menotti
- William Robson Schwartz
- M. Zhou
- Taiyong Li
In Control and Systems Engineering
In Signal Processing
Citations in the corpus
- ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “The PCA technique separates the sources according to the energy contribution to the signal.”
- Lightweight Lossy Compression of Biometric Patterns via Denoising Autoencoders (2015, methodology): “Remarkably, AE considerably outperforms PCA and this is due to its non-linear dimensionality reduction capabilities .”