Dynamical Component Analysis (DyCA) and its application on epileptic EEG
This work addresses seizure detection in epilepsy patients using a new dimensionality reduction technique, but it appears incremental as it builds on existing methods like PCA and ICA.
The authors tackled the problem of dimensionality reduction for deterministic multivariate datasets by introducing Dynamical Component Analysis (DyCA), a method based on solving a generalized eigenvalue problem, and applied it to epileptic EEG data for seizure detection, achieving results in terms of specificity, false discovery rate, and miss rate compared to other algorithms.
Dynamical Component Analysis (DyCA) is a recently-proposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets. It is based on the solution of a generalized eigenvalue problem and therefore straight forward to implement. DyCA is introduced and applied to EEG data of epileptic seizures. The obtained eigenvectors are used to project the signal and the corresponding trajectories in phase space are compared with PCA and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection and the obtained results in terms of specificity, false discovery rate and miss rate are compared to other seizure detection algorithms.