Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 29, Pages: 1-4
R. Bhuvana1*, S. Purushothaman2 and P. Rajeswari3
1 Department of Computer Science, A. M. Jain College, Chennai - 600 114, Tamil Nadu, India; [email protected]
2 Institute of Technology, Haramaya University, Ethiopia; [email protected]
3 Department of ECE, Institute of Technology, Haramaya University, Ethiopia; [email protected]
Depression is common in working personality due to high tension in the working environment. Symptoms can affect dayto-day life and can become very worrying. With true depression, there is a low mood and other symptoms each day for more than two weeks. Symptoms can also become rigorous enough to interfere with normal day-to-day activities. This paper suggests an Artificial Neural Network (ANN) algorithm approach for quicker learning of psychological depression data. Performance of neural network methods for estimating depression state with Echo State Neural Network (ESNN) is presented. Tentative data were collected from the patients with 21 input variables. One target output is used for training the ESNN. The training and testing patterns are made using the data as per Hamilton Rating Scale.The input patterns are pre-processed and presented to the input layer of ANN. The proposed method proves to be a capable system for diagnosis of depression.
Keywords: Depression Data, ESNN, Hamilton Rating Scale
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