PhD Scientific Days 2022

Neurosciences II.

Real-time Algorithm for Detrended Cross-correlation Analysis of Long-range Coupled Processes

Előadó neve

Káposzta, Zalán

Előadó munkahelye

Semmelweis Egyetem

Előadó telefonszáma

+36309033176

Előadó e-mail címe

zalkaposzt@gmail.com

Az előadás címe

Real-time Algorithm for Detrended Cross-correlation Analysis of Long-range Coupled Processes

Szerző(k) neve és munkahelye

MD-PhD Student, Department of Physiology, Budapest

Bemutatás módja

Szóbeli

Szekció

Neurosciences II.

Language of the presentation

English

Preferred session

Neurosciences

Összefoglaló szövege

Examining long-range cross-correlations between processes is of great significance in many fields, including neuroscience. Such analyses are computationally expensive and thus mostly carried out offline. However, many applications such as mental state monitoring require methods providing estimates in real-time. Despite this, no algorithms have been proposed yet to assess long-range coupling online.

We introduce a real-time formula for detrended cross-correlation analysis (DCCA), a well-established and widely used method for assessing long-range cross-correlations. We show that the formula can also be expressed in matrix operations, which allows for performing DCCA between multiple processes simultaneously, as well as online derivation of the detrended cross-correlation coefficients (DCCCs), a standardized version of DCCA. Execution time (ET) and precision of the online methods (pairwise and matrix) was compared with their offline counterparts in a simulation environment using multiple realizations of time series ranging from 28 to 217 datapoints in length. The ETs of online methods were also contrasted in case of increasing number of time series. Finally, to evaluate real-world applicability of the algorithm, we analysed electroencephalography (EEG) recordings. Data was obtained from 22 young, healthy volunteers (12 female, aged 23.9±2.3) in eyes-open (EO) and eyes-closed (EC) resting conditions (14 cortical regions, 256 Hz sampling rate). DCCC analysis was carried out and a linear classifier was trained to see if mental state (EO vs. EC) could be identified online automatically.

ET was reduced on average by three orders of magnitude when comparing the online to the offline implementation, while precision remained unaffected. Simulations indicated that the matrix formula should be preferred over sequential pairwise evaluation when the number of time series exceeds 8. Classification of mental state could be achieved with an accuracy surpassing 79%.

Our novel real-time DCCA formula vastly outperformed the offline implementation in execution time, while maintaining precision. The introduced matrix formula provided additional benefits as the number of time series pairs increased. Based on our results regarding real-world applicability, our real-time formula carries future potential e.g. in automated mental-state monitoring.

University and Doctoral School

Semmelweis University, Doctoral School of Molecular Medicine

Supervisor

Dr. Rácz Frigyes Sámuel

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

6800

Start

15:00

End

15:15

Authors (legacy)

MD-PhD Student, Department of Physiology, Budapest