Abstract
Depression is a common mental illness, which is extremely harmful to individuals and society. Timely and effective diagnosis is very important for patients’ treatments and depression preventions. In this paper, we treat the trajectory of gait as signal, proposing a new direction to detect the depression with gait frequency features based on Hilbert-Huang transform (HHT). Two groups of participants are recruited in this experiment, including 47 healthy people and 54 depressed patients, respectively. We process the gait data with HHT and build the classification models which verification method is leave-one-out. The best result of our work is 91.09% when the model I is adopted and the classifier is SVM. The corresponding specificity and sensitivity are 87.23% and 94.44% respectively. It verifies that the gait frequency of patients with depression is significantly different from that of healthy people, and the frequency domain features of gait are helpful for the diagnosis of depression.
Baobin Li—This work was supported by NSFC under Grant U1536104, 11301504.
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Battista, B.M., Knapp, C., McGee, T., Goebel, V.: Application of the empirical mode decomposition and hilbert-huang transform to seismic reflection data. Geophysics 72(2), H29–H37 (2007)
Darby, J.K., Simmons, N., Berger, P.A.: Speech and voice parameters of depression: a pilot study. J. Commun. Disord. 17(2), 75–85 (1984)
Girard, J.M., Cohn, J.F., Mahoor, M.H., Mavadati, S., Rosenwald, D.P.: Social risk and depression: evidence from manual and automatic facial expression analysis. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–8. IEEE (2013)
Hashim, N.W., Wilkes, M., Salomon, R., Meggs, J., France, D.J.: Evaluation of voice acoustics as predictors of clinical depression scores. J. Voice 31(2), 256–e1 (2017)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995. The Royal Society (1998)
Lemke, M.R., Wendorff, T., Mieth, B., Buhl, K., Linnemann, M.: Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls. J. Psychiatr. Res. 34(4), 277–283 (2000)
Li, H., Kwong, S., Yang, L., Huang, D., Xiao, D.: Hilbert-huang transform for analysis of heart rate variability in cardiac health. IEEE/ACM Trans. Computat. Biol. Bioinf. 8(6), 1557–1567 (2011)
Ooi, K.E.B., Lech, M., Allen, N.B.: Multichannel weighted speech classification system for prediction of major depression in adolescents. IEEE Trans. Biomed. Eng. 60(2), 497–506 (2013)
World Health Organization: Depression and other common mental disorders: global health estimates (2017)
Oweis, R.J., Abdulhay, E.W.: Seizure classification in eeg signals utilizing hilbert-huang transform. Biomed. Eng. Online 10(1), 1–15 (2011)
Pampouchidou, A., Marias, K., Tsiknakis, M., Simos, P., Yang, F., Meriaudeau, F.: Designing a framework for assisting depression severity assessment from facial image analysis. In: 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 578–583. IEEE (2015)
Pampouchidou, A., Simantiraki, O., Vazakopoulou, C.M., Chatzaki, C., Pediaditis, M., Maridaki, A., Marias, K., Simos, P., Yang, F., Meriaudeau, F., et al.: Facial geometry and speech analysis for depression detection. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1433–1436. IEEE (2017)
Rai, V., Mohanty, A.: Bearing fault diagnosis using fft of intrinsic mode functions in hilbert-huang transform. Mechan. Syst. Signal Process. 21(6), 2607–2615 (2007)
Sloman, L., Berridge, M., Homatidis, S., Hunter, D., Duck, T.: Gait patterns of depressed patients and normal subjects. Am. J. Psychiatry 139, 94–97 (1982)
Stoll, A.L., Hausdorff, J.M., Peng, C.K., Goldberger, A.L.: Gait unsteadiness and fall risk in two affective disorders: A preliminary study (2004)
Tribbey, W., Press,W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes 3rd edition: The Art of Scientific Computing. Cambridge University Press, Cambridge (2007). Hardback, 1235 p. ACM (2010). ISBN 978-0-521-88068-8
Yang, Z., Yang, L., Qi, D., Suen, C.Y.: An emd-based recognition method for chinese fonts and styles. Pattern Recogn. Lett. 27(14), 1692–1701 (2006)
Zhou, X., Jin, K., Shang, Y., Guo, G.: Visually interpretable representation learning for depression recognition from facial images. IEEE Trans. Affect. Comput. (2018)
Zong, C., Chetouani, M.: Hilbert-Huang transform based physiological signals analysis for emotion recognition. In: 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 334–339. IEEE (2009)
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Yuan, Y., Li, B., Wang, N., Ye, Q., Liu, Y., Zhu, T. (2019). Depression Identification from Gait Spectrum Features Based on Hilbert-Huang Transform. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_51
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