Open Access Open Access  Restricted Access Subscription or Fee Access

Implementation of Hamilton Rating Scale Depression Data Using Back Propagation Network and Echo STAE Neural Network (BPAESNN) Methods

R. Bhuvana, S. Purushothaman, R. Rajeswari


Depression is a serious and widespread public health challenge. This paper propose neural network algorithm for faster learning of psychological depression data. Implementation of neural networks methods for depression data mining using back propagation algorithm(BPA) and Echo state neural network(ESNN) are presented. Experimental data were collected with 21 input variables and one output for working with artificial neural network(ANN). Using the data collected, the training patterns and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of ANN In order to find the optimum number of nodes required in the hidden layer of an ANN, a method has been proposed, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to ESNN. The network trained with transformed vectors is seen to require the least computational effort. The work proves to be an efficient system for diagnosis of depression.


Hamilton Rating Scale (HRS) Depression Data, BPA, ESNN

Full Text:



Atiya A.F. and Parlos A.G., 2000, New results on recurrent network training: Unifying the algorithms and accelerating convergence. IEEE Trans. Neural Networks, 11(3),PP.697-709

Dmitri Bibitchkov, J Michael Herrmann and Theo Geisel, 2000,, “Synaptic Depression in Associative Memory Networks”, IEEE Conference on Neural Networks”, Vol. 5, No. 1, pp. 50-55.

Elovainio M, Keltikangas-Jarvinen L, Kivimaki M, Pulkki L, Puttonen S, Heponiemi T, Juonala M, Viikari JS and Raitakari OT, 2005, “Depressive symptoms and carotid artery intima-media thickness in young adults: the Cardiovascular Risk in Young Finns Study”, Psychosomatic Medicine,Vol. 67, No. 1, pp. 522-530.

Elovainio M, Keltikangas-Jarvinen L, Pulkki-Råback L, Kivimaki M, Puttonen S, Viikari L, Rasanen L, Mansikkaniemi K, Viikari J and Raitakari OT, 2006, “Depressive symptoms and Creactive protein: the Cardiovascular Risk in Young Finns Study”, Psychological Medicine , Vol. 36, No. 1, pp. 797-805.

Fortuna L., Graziani S., Lopresti M. and Muscato G, 1992, “Improving back-propagation learning using auxiliary neural networks”, International Journal of Control, Vol. 55, No. 4, pp. 793-807.

Hamilton, M.,1960, „A rating scale for depression‟, Journal of Neurology and Neurosurgery, 23, pp. 56-61.

Hirose Y, Yamashita KY and Hijiya S ,1991, “Back-propagation algorithm which varies the number of hidden units”, Neural Networks, Vol. 4, No. 1, pp. 61-66.

Hirotaka INOUE, , 2003, “Efficient Pruning Method for Ensemble Self-Generating Neural Networks”, Systemics, Cybernetics and Informatics, Vol. 1, No. 6pp. 72-77.

Hornik K, Stinchcombe M and White H, 1989, “Multilayer feedforward networks are universal approximators”, Neural Networks, Vol. 2, No. 5, pp. 359-366.

Jaeger H.,2001a,The "echo state" approach to analysing and training recurrent neural networks. GMD Report 148, GMD - German National Research Institute for Computer Science

Lukoševičius M. and Jaeger H.,2009, Reservoir Computing Approaches to Recurrent Neural Network Training (draft version). Computer Science Review 3(3),PP. 127-149.

Michael D. Greicius., Ben Krasnow., Allan L. Reiss., and Vinod Menon., January 2003,Functional connectivity in the resting brain: A network analysis of the default mode hypothesis,Journal of Neuro Science, Vol 100 no 1 pp 253–258.

Purushothaman S, Srinivasa YG, “A back-propagation algorithm applied to tool wear monitoring”, International Journal of M/C Tools and Manufacturing, Vol. 34, No. 5, 1994, pp. 625-631.

Ramaraj E and Radha P, “Depression Data Mining using Back-Propagation Algorithm”, IEEE International Advanced Computing Conference, Thapur University, Patiala. The Proceedings published in IEEE Explorer, March 6-7, 2009.

Sher L, Mann JJ, Traskman-Bendz L Winchel R, Huang YY, Fertuck E and Stanley BH, “Lower cerebrospinal fluid homovanillic acid levels in depressed suicide attempters”, Journal of Affective Disorders, Vol. 90, No. 1, 2006, pp. 83-89

Weishui Wan, Kotaro Hirasawa, Jinglu Hu and Chunzhi Jin, “A New Method to Prune the Neural Network”, IJCNN, Vol. 6, No. 1, 2000, pp. 449-454.

Ying-jie Li., Fei-yan Fan. January 2006, Classification of Schizophrenia and Depression by EEG with ANNs, Engineering in Medicine and Biology Society, pp 2679 – 2682.

YANG Sheng-yue., FAN Xiao-ping.,PENG Zan.,Yan-ping.JI.,2005, Dynamic BP neural networks based depression diagnosis system, Journal of Railway Science and Engineering.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.