Abstract
Depression negatively affects the daily life of an individual and may even lead to suicidal tendencies. The problem is compounded by the scarcity of trained psychologists and psychiatrists in developing countries due to which many cases go undetected. The automated diagnosis of depression can, therefore, assist clinicians to screen the patients and help them to handle the symptoms. The advent of wearable devices in the past decade has helped in capturing signals, which can be used to diagnose depression. This work uses a publicly available dataset and develops a model based on the distribution of microstructures from the temporal data to accomplish the given task. The results are encouraging and better than the state-of-the-art. An accuracy of 86.90% is obtained by using the proposed pipeline. This work is part of a larger project that aims to detect depression using multi-modality data.
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References
What is Depression?. https://www.psychiatry.org/patients-families/depression/what-is-depression. Accessed 20 Oct 2023
Depression. https://www.who.int/health-topics/depression#tab=tab_1. Accessed 20 Oct 2023
Professional, C.C.M.: Depression. https://my.clevelandclinic.org/health/diseases/9290-depression. Accessed 20 Oct 2023
Depressive disorder (depression). https://www.who.int/news-room/fact-sheets/detail/depression. Accessed 20 Oct 2023
Depression. https://who.int/india/health-topics/depression. Accessed 20 Oct 2023
Garcia-Ceja, E., et al.: Depresjon. In: Proceedings of the 9th ACM Multimedia Systems Conference (2018). https://doi.org/10.1145/3204949.3208125
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29, 51–59 (1996). https://doi.org/10.1016/0031-3203(95)00067-4
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002). https://doi.org/10.1109/tpami.2002.1017623
Ojala, T., Pietikäinen, M., Mäenpää, T.: A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: Singh, S., Murshed, N., Kropatsch, W. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 399–408. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44732-6_41
Bhasin, H., Agrawal, R.K.: A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment. BMC Med. Inform. Decis. Making 20 (2020). https://doi.org/10.1186/s12911-020-1055-x
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/bf00994018
Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Stat. 36 (2008). https://doi.org/10.1214/009053607000000677
Thompson, M., Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. Leonardo 7, 370 (1974). https://doi.org/10.2307/1573081
Bhasin, H.: Machine Learning for Beginners. BPB Publications (2020)
Chikersal, P., et al.: Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing. ACM Trans. Comput.-Hum. Interact. 28, 1–41 (2021). https://doi.org/10.1145/3422821
Doryab, A., Min, J.K., Wiese, J., Zimmerman, J., Hong, J.I.: Detection of behavior change in people with depression. In: National Conference on Artificial Intelligence (2014). https://doi.org/10.1184/r1/6469988.v1
Ben-Zeev, D., Scherer, E.A., Wang, R., Xie, H., Campbell, A.T.: Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health. Psychiatr. Rehabil. J. 38, 218–226 (2015). https://doi.org/10.1037/prj0000130
Saeb, S., Lattie, E.G., Schueller, S.M., Kording, K., Mohr, D.C.: The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4, e2537 (2016). https://doi.org/10.7717/peerj.2537
Saeb, S., et al.: Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res. 17, e175 (2015). https://doi.org/10.2196/jmir.4273
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., Weidt, S.: Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR Mhealth Uhealth 4, e111 (2016). https://doi.org/10.2196/mhealth.5960
Canzian, L., Musolesi, M.: Trajectories of depression. In: UbiComp 2015: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (2015). https://doi.org/10.1145/2750858.2805845
Farhan, A.A., et al.: Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In: IEEE Wireless Health (WH) (2016). https://doi.org/10.1109/wh.2016.7764553
Bhasin, H., Kumar, N., Singh, A., Sharma, M., Beniwal, R.P.: Kullback-Leibler divergence based method for depression diagnosis using video data. In: 14th International Conference on Computing, Communication and Networking Technologies (2023)
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Bhasin, H., Chirag, Kumar, N., Thakur, H.K. (2024). Depression Detection Using Distribution of Microstructures from Actigraph Information. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2053. Springer, Cham. https://doi.org/10.1007/978-3-031-56700-1_14
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