Key result
A proposed algorithm for grouping principal components in singular spectrum analysis of tremorograms showed reasonable correspondence with traditional classification methods.
Why the study?
Tremorograms have a complex structure requiring advanced signal processing to extract diagnostically significant features for evaluating the motor parts of the central nervous system.
A novel grouping algorithm based on singular spectrum analysis can effectively extract diagnostically significant features from tremorograms to evaluate neuromotor reactions.
SSA may enhance tremorogram analysis for neuromotor assessment; leaves open validation in clinical cohorts before adoption.
Singular spectrum analysis (SSA) is a method of time series analysis and is used in various fields, including medicine. A tremorogram is a biological signal that allows evaluation of a person’s neuromotor reactions in order to infer the state of the motor parts of the central nervous system (CNS). A tremorogram has a complex structure, and its analysis requires the use of advanced methods of signal processing and intelligent analysis. The paper’s novelty lies in the application of the SSA method to extract diagnostically significant features from tremorograms with subsequent evaluation of the state of the motor parts of the CNS. The article presents the application of a method of singular spectrum decomposition, comparison of known variants of classification, and grouping of principal components for determining the components of the tremorogram corresponding to the trend, periodic components, and noise. After analyzing the results of the SSA of tremorograms, we proposed a new algorithm of grouping based on the analysis of singular values of the trajectory matrix. An example of applying the SSA method to the analysis of tremorograms is shown. Comparison of known clustering methods and the proposed algorithm showed that there is a reasonable correspondence between the proposed algorithm and the traditional methods of classification and pairing in the set of periodic components.
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Bureneva et al. (2022) studied Neuromotor reactions. Singular spectrum analysis (SSA) with a proposed grouping algorithm vs. Traditional methods of classification and pairing was evaluated on Extraction of diagnostically significant features from tremorograms. A proposed algorithm for grouping principal components in singular spectrum analysis of tremorograms showed reasonable correspondence with traditional classification methods.
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