research-article

Open access

Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients

Authors:

Enrique Garcia-Ceja,

Michael Riegler,

Petter Jakobsen,

Tine Nordgreen,

Ketil J. Oedegaard,

Ole Bernt FasmerAuthors Info & Claims

MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference

Pages 472 - 477

https://doi.org/10.1145/3204949.3208125

Published: 12 June 2018 Publication History

Abstract

Wearable sensors measuring different parts of people's activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients' depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.

References

[1]

Lauren B Alloy, Tommy H Ng, Madison K Titone, and Elaine M Boland. 2017. Circadian rhythm dysregulation in bipolar spectrum disorders. Current psychiatry reports 19, 4 (2017), 21.

[2]

Atiyeh Bayani, Fatemeh Hadaeghi, Sajad Jafari, and Greg Murray. 2017. Critical slowing down as an early warning of transitions in episodes of bipolar disorder: A simulation study based on a computational model of circadian activity rhythms. Chronobiology international (2017).

[3]

William Bechtel. 2015. Circadian rhythms and mood disorders: are the phenomena and mechanisms causally related? Frontiers in psychiatry 6 (2015), 118.

[4]

Jan O Berle, Erik R Hauge, Ketil J Oedegaard, Fred Holsten, and Ole B Fasmer. 2010. Actigraphic registration of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression. BMC research notes 3, 1 (2010), 149.

[5]

Christopher Burton, Brian McKinstry, Aurora Szentagotai Tătar, Antoni Serrano-Bianco, Claudia Pagliari, and Maria Wolters. 2013. Activity monitoring in patients with depression: a systematic review. Journal of affective disorders 145, 1 (2013).

[6]

Ole B Fasmer, Hagop S Akiskal, John R Kelsoe, and Ketil J Oedegaard. 2009. Clinical and pathophysiological relations between migraine and mood disorders. Current Psychiatry Reviews 5, 2 (2009), 93--109.

[7]

Enrique Garcia-Ceja, Venet Osmani, and Oscar Mayora. 2016. Automatic stress detection in working environments from smartphones' accelerometer data: a first step. IEEE journal of biomedical and health informatics 20, 4 (2016), 1053--1060.

[8]

Vishwesha Guttal and Ciriyam Jayaprakash. 2008. Changing skewness: an early warning signal of regime shifts in ecosystems. Ecology letters 11, 5 (2008), 450--460.

[9]

CJ Hawley, TM Gale, and T Sivakumaran. 2002. Defining remission by cut off score on the MADRS: selecting the optimal value. Journal of affective disorders 72, 2 (2002), 177--184.

[10]

RM Hirschfeld. 2014. Differential diagnosis of bipolar disorder and major depressive disorder. Journal of affective disorders 169 (2014), S12--S16.

[11]

Nathalie Japkowicz and Shaju Stephen. 2002. The class imbalance problem: A systematic study. Intelligent data analysis 6, 5 (2002), 429--449.

Digital Library

[12]

N Keshan, PV Parimi, and Isabelle Bichindaritz. 2015. Machine learning for stress detection from ECG signals in automobile drivers. In Big Data (Big Data), 2015 IEEE International Conference on. IEEE, 2661--2669.

Digital Library

[13]

Sotiris Kotsiantis, Dimitris Kanellopoulos, Panayiotis Pintelas, and others. 2006. Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering 30, 1 (2006), 25--36.

[14]

Dominic Landgraf, Michael J McCarthy, and David K Welsh. 2014. The role of the circadian clock in animal models of mood disorders. Behavioral neuroscience 128, 3 (2014), 344.

[15]

L. B. Leng, L. B. Giin, and W. Y. Chung. 2015. Wearable driver drowsiness detection system based on biomedical and motion sensors. In 2015 IEEE SENSORS. 1--4.

[16]

Peter M Lewinsohn, Ari Solomon, John R Seeley, and Antonette Zeiss. 2000. Clinical implications of "subthreshold" depressive symptoms. Journal of abnormal psychology 109, 2 (2000), 345.

[17]

Eva M Marco, Elena Velarde, Ricardo Llorente, and Giovanni Laviola. 2016. Disrupted circadian rhythm as a common player in developmental models of neuropsychiatric disorders. In Neurotoxin Modeling of Brain Disorders --- Life-long Outcomes in Behavioral Teratology. Springer, 155--181.

[18]

Stuart A Montgomery and MARIE Asberg. 1979. A new depression scale designed to be sensitive to change. The British journal of psychiatry 134, 4 (1979), 382--389.

[19]

Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews, David Ellis, Nicola Bellotto, Radu Dobrescu, and Jose Manuel Ferrandez. 2017. Stress detection using wearable physiological and sociometric sensors. International Journal of Neural Systems 27, 02 (2017), 1650041.

[20]

Matthias J Müller, Hubertus Himmerich, Barbara Kienzle, and Armin Szegedi. 2003. Differentiating moderate and severe depression using the Montgomery-Åsberg depression rating scale (MADRS). Journal of affective disorders (2003).

[21]

NICE. 2009. National Institute for Health and Clinical Excellence. Depression in adults: recognition and management. NICE guideline CG90. https://www.nice.org.uk/guidance/cg90. (2009). {last visited, February 14, 2018}.

[22]

The National Institute of Mental Health IRC. 2018. Definitions of the RDoC Domains and Constructs. (2018). https://www.nimh.nih.gov/research-priorities/rdoc/definitions-of-the-rdoc-domains-and-constructs.shtml

[23]

Mark Olfson, Benjamin G Druss, and Steven C Marcus. 2015. Trends in mental health care among children and adolescents. New England Journal of Medicine 372, 21 (2015), 2029--2038.

[24]

Frank J Penedo and Jason R Dahn. 2005. Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Current opinion in psychiatry 18, 2 (2005), 189--193.

[25]

Stephen Pilling, Ian Anderson, David Goldberg, Nicholas Meader, Clare Taylor, Two Guideline Development Groups, and others. 2009. Depression in adults, including those with a chronic physical health problem: summary of NICE guidance. BMJ 339, 10.1136 (2009).

[26]

Guilherme V Polanczyk, Giovanni A Salum, Luisa S Sugaya, Arthur Caye, and Luis A Rohde. 2015. Annual Research Review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. Journal of Child Psychology and Psychiatry 56, 3 (2015), 345--365.

[27]

Nadja Razavi, Helge Horn, Philipp Koschorke, Simone Hügli, Oliver Höfle, Thomas Müller, Werner Strik, and Sebastian Walther. 2011. Measuring motor activity in major depression: the association between the Hamilton Depression Rating Scale and actigraphy. Psychiatry research 190, 2 (2011), 212--216.

[28]

Marten Scheffer, Jordi Bascompte, William A Brock, Victor Brovkin, Stephen R Carpenter, Vasilis Dakos, Hermann Held, Egbert H Van Nes, Max Rietkerk, and George Sugihara. 2009. Early-warning signals for critical transitions. Nature 461, 7260 (2009), 53.

[29]

Jan Scott, Greg Murray, Chantal Henry, Gunnar Morken, Elizabeth Scott, Jules Angst, Kathleen R Merikangas, and Ian B Hickie. 2017. Activation in bipolar disorders: a systematic review. JAMA psychiatry 74, 2 (2017), 189--196.

[30]

Jean M Twenge. 2015. Time period and birth cohort differences in depressive symptoms in the US, 1982--2013. Social Indicators Research 121, 2 (2015), 437--454.

[31]

Gaetano Valenza, Mimma Nardelli, Antonio Lanata, Claudio Gentiii, Gilles Bertschy, Rita Paradiso, and Enzo Pasquale Scilingo. 2014. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE Journal of Biomedical and Health Informatics 18, 5 (2014), 1625--1635.

[32]

Marianne Agergaard Vammen, Sigurd Mikkelsen, Åse Marie Hansen, Jens Peter Bonde, Matias B Grynderup, Henrik Kolstad, Linda Kærlev, Ole Mors, Reiner Rugulies, and Jane Frlund Thomsen. 2016. Emotional demands at work and the risk of clinical depression: A longitudinal study in the Danish public sector. Journal of occupational and environmental medicine 58, 10 (2016), 994--1001.

[33]

Nicola Vanello, Andrea Guidi, Claudio Gentili, Sandra Werner, Gilles Bertschy, Gaetano Valenza, Antonio Lanata, and Enzo Pasquale Scilingo. 2012. Speech analysis for mood state characterization in bipolar patients. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. IEEE, 2104--2107.

Cited By

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https://doi.org/10.4018/979-8-3693-5261-8.ch016
Kovtun ORosenthal S(2024)Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolationPLOS Mental Health10.1371/journal.pmen.00001241:4(e0000124)Online publication date: 25-Sep-2024
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Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients

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cover image ACM Conferences

MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference

June 2018

604 pages

ISBN:9781450351928

DOI:10.1145/3204949

General Chair:
Pablo Cesar
Centrum Wiskunde & Informatica, The Netherlands
,
Program Chairs:
Michael Zink
University of Massachusetts Amherst
,
Niall Murray
Athlone Institute of Technology, Ireland

Copyright © 2018 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Published: 12 June 2018

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Norwegian Research Council

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MMSys '18

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MMSys '18: 9th ACM Multimedia Systems Conference

June 12 - 15, 2018

Amsterdam, Netherlands

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Overall Acceptance Rate 176 of 530 submissions, 33%

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Kovtun ORosenthal S(2024)Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolationPLOS Mental Health10.1371/journal.pmen.00001241:4(e0000124)Online publication date: 25-Sep-2024
https://doi.org/10.1371/journal.pmen.0000124
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https://dl.acm.org/doi/10.1145/3702038.3702096
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Hu YChen JChen JWang WZhao SHu X(2024)An Ensemble Classification Model for Depression Based on Wearable Device Sleep DataIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.325860128:5(2602-2612)Online publication date: May-2024
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Sarder MRaihan MDebnath AIslam MSamrat NEfty RHasan MFahimuzzaman M(2024)Predicting Depressive Behavior with Monitoring Activity Data Using Machine Learning and Feature Selection Approaches2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)10.1109/ICEEICT62016.2024.10534476(284-289)Online publication date: 2-May-2024
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V. SS.D. M(2024)Optimal interval and feature selection in activity data for detecting attention deficit hyperactivity disorderComputers in Biology and Medicine10.1016/j.compbiomed.2024.108909179:COnline publication date: 18-Oct-2024
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