» Articles » PMID: 21297977

Nonlinear Analysis of Motor Activity Shows Differences Between Schizophrenia and Depression: a Study Using Fourier Analysis and Sample Entropy

Overview
Journal PLoS One
Date 2011 Feb 8
PMID 21297977
Citations 42
Authors
Affiliations
Soon will be listed here.
Abstract

The purpose of this study has been to describe motor activity data obtained by using wrist-worn actigraphs in patients with schizophrenia and major depression by the use of linear and non-linear methods of analysis. Different time frames were investigated, i.e., activity counts measured every minute for up to five hours and activity counts made hourly for up to two weeks. The results show that motor activity was lower in the schizophrenic patients and in patients with major depression, compared to controls. Using one minute intervals the depressed patients had a higher standard deviation (SD) compared to both the schizophrenic patients and the controls. The ratio between the root mean square successive differences (RMSSD) and SD was higher in the schizophrenic patients compared to controls. The Fourier analysis of the activity counts measured every minute showed that the relation between variance in the low and the high frequency range was lower in the schizophrenic patients compared to the controls. The sample entropy was higher in the schizophrenic patients compared to controls in the time series from the activity counts made every minute. The main conclusions of the study are that schizophrenic and depressive patients have distinctly different profiles of motor activity and that the results differ according to period length analysed.

Citing Articles

OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD.

Garcia-Ceja E, Stautland A, Riegler M, Halvorsen P, Hinojosa S, Ochoa-Ruiz G Sci Data. 2025; 12(1):32.

PMID: 39779688 PMC: 11711611. DOI: 10.1038/s41597-025-04384-3.


Reduced multiscale complexity of daily behavioral dynamics in autism spectrum disorder.

Nakamura T, Sumiyoshi T, Kamio Y, Takahashi H PCN Rep. 2024; 3(4):e70016.

PMID: 39329059 PMC: 11423455. DOI: 10.1002/pcn5.70016.


Feasibility of Sarcopenia Diagnosis Using Stimulated Muscle Contraction Signal in Hemiplegic Stroke Patients.

Ji Y, Yoon M, Song K, Choi S, Lee H, Jung J Brain Neurorehabil. 2024; 17(2):e10.

PMID: 39113921 PMC: 11300960. DOI: 10.12786/bn.2024.17.e10.


The sleep-circadian interface: A window into mental disorders.

Meyer N, Lok R, Schmidt C, Kyle S, McClung C, Cajochen C Proc Natl Acad Sci U S A. 2024; 121(9):e2214756121.

PMID: 38394243 PMC: 10907245. DOI: 10.1073/pnas.2214756121.


Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

Vairavan S, Rashidisabet H, Li Q, Ness S, Morrison R, Soares C Sci Rep. 2023; 13(1):18596.

PMID: 37903878 PMC: 10616277. DOI: 10.1038/s41598-023-44592-8.


References
1.
Hornero R, Abasolo D, Jimeno N, Sanchez C, Poza J, Aboy M . Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects. IEEE Trans Biomed Eng. 2006; 53(2):210-8. DOI: 10.1109/TBME.2005.862547. View

2.
Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov P, Mark R . PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000; 101(23):E215-20. DOI: 10.1161/01.cir.101.23.e215. View

3.
Tschacher W, Scheier C, Hashimoto Y . Dynamical analysis of schizophrenia courses. Biol Psychiatry. 1997; 41(4):428-37. DOI: 10.1016/S0006-3223(96)00039-X. View

4.
Littner M, Kushida C, Anderson W, Bailey D, Berry R, Davila D . Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002. Sleep. 2003; 26(3):337-41. DOI: 10.1093/sleep/26.3.337. View

5.
Woyshville M, Lackamp J, Eisengart J, Gilliland J . On the meaning and measurement of affective instability: clues from chaos theory. Biol Psychiatry. 1999; 45(3):261-9. DOI: 10.1016/s0006-3223(98)00152-8. View