Classification of Depressive Episodes Using Nighttime Data: Multivariate and Univariate Analysis

Mental disorders like depression represent 28% of global disability, it affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features, the results of the classification are specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.

1. Depression and other common mental disorders: global health estimates. World Health Organization, 2017, 24 p.

2. Espinosa-Aguilar J. Caraveo-Anduaga M., Zamora-Olvera A. et al. Guía de práctica clínica para el diagnóstico y tratamiento de depresión en los adultos mayors. Salud Mental, vol. 30, no. 6, 2007, pp. 69-80 (in Spanish).

3. S.A. Montgomery and M. Asberg. A new depression scale designed to be sensitive to change. The British Journal of Psychiatry, vol. 134, no. 4, 1979, pp. 382-389.

4. Armitage R. Sleep and circadian rhythms in mood disorders. Acta Psychiatrica Scandinavica, vol. 115, issue s403, 2007, pp. 104-115.

5. Koffel E., Polusny M.A., Arbisi P.A., and Erbes C.R. Pre-deployment day time and nighttime sleep complaints as predictors of post-deployment ptsd and depression in national guard troops. Journal of Anxiety Disorders, vol. 27, no. 5, 2013, pp. 512-519.

6. Wichniak A. Wierzbicka A., Walecka M., and Jernajczyk W. Effects of antidepressants on sleep. Current Psychiatry Reports, vol. 19, no. 9, 2017, article no. 63.

7. Kuhs H. and Reschke D.. Psychomotor activity in unipolar and bipolar depressive patients. Psychopathology, vol. 25, no. 2, 1992, pp. 109-116.

8. Shatte A.B., Hutchinson D.M., & Teague S.J. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, vol. 49, no. 9, 2019, pp. 1426-1448.

9. Frogner J.I., Noori F.M., Halvorsen P. et al. One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression. In Proc. of the 4th International Workshop on Multimedia for Personal Health & Health Care, 2019, pp. 9-15.

10. García-Ceja E., Riegler M., Jakobsen P. et al. Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients. In Proc. of the 9th ACM Multimedia Systems Conference, 2018, pp.472-477.

11. Zanella-Calzada L.A., Galván-Tejada C.E., Chávez-Lamas N.M. et al. Feature extraction in motor activity signal: Towards a depression episodes detection in unipolar and bipolar patients. Diagnostics, vol. 9, no. 1, 2019, pp. 1-13.

12. Schuch F.B., Vancampfort D., Firth J. et al. Physical activity and incident depression: a meta-analysis of prospective cohort studies. American Journal of Psychiatry, vol. 175, no. 7, 2018, pp. 631-648.

13. Gruenerbl A., Osmani V., Bahle G. et al. Using smartphone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. In Proc. of the 5th Augmented Human International Conference, 2014, p. 1-8.

14. Murphy M.J. and Peterson M.J. Sleep disturbances in depression. Sleep Medicine Clinics, vol. 10, no. 1, 2015, pp. 17-23.

15. Guglielmi O., Magnavita N., and Garbarino S. Sleep quality, obstructive sleep apnea, and psychological distress in truck drivers: A cross-sectional study. Social Psychiatry and Psychiatric Epidemiology, vol. 53, no. 5, 2018, pp. 531–536.

16. Dåderman A. and Rosander S. Evaluating Frameworks for Implementing Machine Learning in Signal Processing: A Comparative Study of CRISP-DM, SEMMA and KDD. Bachelor’s Thesis. KTH, School of Electrical Engineering and Computer Science, 2018, 43 p.

17. Srinivasan R., Chen C., and Cook D. Activity recognition using actigraph sensor. In Proc. of the Fourth Int. Workshop on Knowledge Discovery from Sensor Data, 2010, pp. 25-28.

18. Garcia-Ceja E., Riegler M., Jakobsen P. et al. Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients. In Proc. of the 9th ACM Multimedia Systems Conference, 2018, pp. 472-477.

19. Vijayashree J. & Sultana H. P. A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier. Programming and Computer Software, vol. 44, no. 6, 2018, pp. 388-397.