Singular Spectrum Analysis of Tremorograms for Human Neuromotor Reaction Estimation
Mathematics
; 10(11):1794, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1892915
ABSTRACT
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.
Mathematics; time series analysis; singular spectrum analysis (SSA); principal component analysis; time series classification; Physiology; Feature extraction; Classification; Signal processing; Wavelet transforms; Spectrum analysis; Blood pressure; Clustering; Pandemics; Fractals; Decomposition; Algorithms; Nervous system; Noise; Methods; Central nervous system; Time series; Evaluation; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Mathematics
Year:
2022
Document Type:
Article
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