Abstract
The worldwide havoc of today’s world: depression, is increasing in this era. Depression is not any specific disease rather the determinant factor in the onset of numerous terrible diseases. With the increase in automation and artificial intelligence, it has become easier to predict depression before a much earlier time. The machine learning techniques are used in the classification of EEG for the prediction of different neuro-problems. EEG signals are the brain waves which can easily detect any abnormalities occurring in the brain waves, thereby making it easier to predict the seizure formation or depression. Proposed work uses the EEG signals for the analysis of brain waves, thereby predicting depression. In this paper, we have compared two widely used benchmark models, i.e., the k-NN and the ANN for the prediction of depression with an accuracy of 85%. This method will help doctors and medical associates in predicting diseases before the onset of its extreme phase, as well as assist them in providing the best treatments, possible in proper time.
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Mohanty, N.P., Dash, S.S., Sobhan, S., Swarnkar, T. (2021). Prediction of Depression Using EEG: A Comparative Study. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_1
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