Correlations in Complex Systems

Yulmetyev, Renat M.; Hänggi, Peter

doi:10.1007/978-1-4614-1800-9_46

Cited by 1 publication

(1 citation statement)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long-memory processes describing complex systems [1,39] and the analysis of long-term correlations (LTC) in related signals [21] play an important role in many research contexts. For instance, we may consider investigations in EEG signals [20], in the analysis subthalamic nucleus of patients with Parkinson's disease [16], in DNA sequences [30], in postural sway of humans [8], and in construction engineering [29].…”

Section: Introductionmentioning

confidence: 99%

Discrimination and Characterization of Parkinsonian Rest Tremors by Analyzing Long-Term Correlations and Multifractal Signatures

Livi

Sadeghian

2016

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

In this paper, we analyze 48 signals of rest tremor velocity related to 12 distinct subjects affected by Parkinson's disease. The subjects belong to two different groups, high-and low-amplitude rest tremors, with four and eight subjects, respectively. Each subject has been tested in four settings given by combining the use of deep brain stimulation and L-DOPA medication. We develop two main feature-based representations of such signals, which are obtained by considering (i) the long-term correlations and multifractal properties, and (ii) the power spectra. Such feature-based representations are initially utilized for the purpose of characterizing the subjects under different settings. Our results show that the effect of medication is clearly recognizable. In addition, when medication is used, we observe a change in the related signals from anti-correlated to long-term, positively correlated. In agreement with previous studies, we observe that deep brain stimulation does not significantly characterize neither of the two groups regardless of the adopted representation. On the other hand, the medication effect yields statistically significant differences in both high-and low-amplitude tremor groups. We successively test several different instances of the two featurebased representations of the signals in the setting of supervised classification and (nonlinear) feature transformation. We consider three different classification problems, involving the recognition of (i) the presence of medication, (ii) the use of deep brain stimulation, and (iii) the membership to the high-and low-amplitude tremor groups. Classification results show that the use of medication can be discriminated with higher accuracy, considering many of the feature-based representations. Notably, we show that the best results are obtained with a parsimonious, two-dimensional representation encoding the long-term correlations and multifractal character of the signals.

show abstract