Key result
Generalized SSA outperforms conventional SSA for leakage reduction in synthetic and EEG signals.
Why the study?
Default use of a rectangular window in conventional singular spectrum analysis corrupts frequency characteristics of components, resulting in spectral leakage.
A generalized singular spectrum analysis model using a tapered window improves spectral leakage reduction compared to conventional SSA.
May enhance EEG leakage reduction; leaves open clinical validation before cardiovascular use.
Singular spectrum analysis (SSA) is widely used for analyzing non-stationary signals. The SSA can be regarded as a finite impulse response (FIR) filter bank where a set of adaptive FIR filters is used to decompose the signal into several meaningful components. Some SSA components would be selected to reconstruct the signal while others would be discarded. However, the default use of a rectangular window in the conventional SSA would corrupt the frequency characteristics of the SSA components, resulting in spectral leakage. To tackle this problem, this study proposes a generalized SSA model that uses a tapered window. The window shape, determined by a non-negative real value (α), results in different levels of leakage. A positive integer parameter (p) is introduced into the SSA trajectory matrix to reduce the edge effect in the reconstructed SSA components. A two-stage grid search method is applied to find the best pair of (α,p). Experiments with synthetic signal and real electroencephalogram (EEG) signal showed that compared with the conventional SSA, the generalized SSA model with an optimal pair of (α,p) has better leakage reduction performance.
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Gu et al. (2023) studied EEG signal analysis. Generalized singular spectrum analysis (SSA) model with a tapered window vs. Conventional SSA with a rectangular window was evaluated on Leakage reduction performance. The generalized singular spectrum analysis model with an optimal pair of parameters demonstrated better leakage reduction performance compared with conventional SSA in synthetic and EEG signals.
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