rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series

2020 ◽  
pp. 1-14
Author(s):  
Johann R. Kleinbub ◽  
Fabian T. Ramseyer
Keyword(s):  
Time Series ◽  
Energy Analysis ◽  
R Package ◽  
Analysis Time ◽  
Motion Energy

Depression Severity Is Related to Less Gross Body Movement: A Motion Energy Analysis

2021 ◽  
pp. 1-7
Author(s):  
Anna Sandmeir ◽  
Désirée Schoenherr ◽  
Uwe Altmann ◽  
Christoph Nikendei ◽  
Henning Schauenburg ◽  
...  
Keyword(s):  
Depressive Disorder ◽  
Energy Analysis ◽  
Rating Scale ◽  
Body Movement ◽  
Psychomotor Retardation ◽  
Diagnostic Interview ◽  
Depression Severity ◽  
Depressed Patients ◽  
Motion Energy ◽  
Objectively Measured

Psychomotor retardation is a well-known clinical phenomenon in depressed patients that can be measured in various ways. This study aimed to investigate objectively measured gross body movement (GBM) during a semi-structured clinical interview in patients with a depressive disorder and its relation with depression severity. A total of 41 patients with a diagnosis of depressive disorder were assessed both with a clinician-rated interview (Hamilton Depression Rating Scale) and a self-rating questionnaire (Beck Depression Inventory-II) for depression severity. Motion energy analysis (MEA) was applied on videos of additional semi-structured clinical interviews. We considered (partial) correlations between patients’ GBM and depression scales. There was a significant, moderate negative correlation between both measures for depression severity (total scores) and GBM during the diagnostic interview. However, there was no significant correlation between the respective items assessing motor symptoms in the clinician-rated and the patient-rated depression severity scale and GBM. Findings imply that neither clinician ratings nor self-ratings of psychomotor symptoms in depressed patients are correlated with objectively measured GBM. MEA thus offers a unique insight into the embodied symptoms of depression that are not available via patients’ self-ratings or clinician ratings.

scholarly journals Fourier motion energy analysis in macaque MT

2010 ◽  
Vol 3 (9) ◽  
pp. 200-200
Author(s):  
B. Krekelberg ◽  
K. Dobkins ◽  
T. D Albright
Keyword(s):  
Energy Analysis ◽  
Motion Energy

Time Series

2021 ◽  
pp. 288-291
Author(s):  
Jean-Frédéric Morin ◽  
Christian Olsson ◽  
Ece Özlem Atikcan
Keyword(s):  
Time Series ◽  
Social Change ◽  
Time Series Analysis ◽  
Longitudinal Analysis ◽  
Analysis Time ◽  
Social Phenomena ◽  
Series Analysis ◽  
Level Of Analysis ◽  
Patterns Of Recurrence ◽  
Over Time

This chapter focuses on time series analysis, a statistical method of longitudinal analysis which is suitable if researchers are interested in the temporality of social phenomena and want to analyse social change and patterns of recurrence over time. In contrast to other statistical methods of longitudinal analysis, time series analysis can be applied even if researchers have only a few cases (maybe even only one) and only a few (maybe even only one) variables. Time series can be built for any level of analysis, as cases can be persons, but are usually organizations or countries. In order to build a time series, the variables need to have been measured several times over a given period, and for each measurement one needs to know the measurement date. There are different goals when doing time series analysis, which can be used in descriptive, explanatory, and interpretive approaches.

scholarly journals KarsTS: an R package for microclimate time series analysis

2019 ◽  
Vol 12 (4) ◽  
pp. 685-697
Author(s):  
M. Sáez ◽  
C. Pla ◽  
S. Cuezva ◽  
D. Benavente
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
R Package ◽  
Series Analysis

scholarly journals TSIS: an R package to infer alternative splicing isoform switches for time-series data

2017 ◽  
Vol 33 (20) ◽  
pp. 3308-3310 ◽  
Author(s):  
Wenbin Guo ◽  
Cristiane P G Calixto ◽  
John W S Brown ◽  
Runxuan Zhang
Keyword(s):  
Time Series ◽  
Alternative Splicing ◽  
Time Series Data ◽  
R Package ◽  
Series Data

scholarly journals Identification and visualisation of differential isoform expression in RNA-seq time series

2017 ◽  
Author(s):  
María José Nueda ◽  
Jordi Martorell-Marugan ◽  
Cristina Martí ◽  
Sonia Tarazona ◽  
Ana Conesa
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Expression Patterns ◽  
R Package ◽  
Series Data ◽  
Rna Seq ◽  
Sequencing Technologies ◽  
Isoform Expression ◽  
Time Series Data Analysis ◽  
Differential Isoform Expression

AbstractAs sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. The package is freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).

scholarly journals SADI: Sequence Analysis Tools for Stata

2017 ◽  
Vol 17 (3) ◽  
pp. 546-572 ◽  
Author(s):  
Brendan Halpin
Keyword(s):  
Time Series ◽  
Sequence Analysis ◽  
Life Course ◽  
Sequence Data ◽  
R Package ◽  
Optimal Matching ◽  
Matching Algorithm ◽  
Analysis Tools ◽  
Categorical Time Series ◽  
Main Components

The SADI package provides tools for sequence analysis, which focuses on the similarity and dissimilarity between categorical time series such as life-course trajectories. SADI‘s main components are tools to calculate intersequence distances using several different algorithms, including the optimal matching algorithm, but it also includes utilities to graph, summarize, and manage sequence data. It provides similar functionality to the R package TraMineR and the Stata package SQ but is substantially faster than the latter.

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