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Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders.
With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification.
Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject.
by Vijaykumar Janga | Prof. E Sreenivasa Reddy "A review on Machine Learning Techniques for Neurological disorders estimation by Analyzing EEG Waves"
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017,
URL: http://www.ijtsrd.com/papers/ijtsrd7082.pdf
Direct Link - http://www.ijtsrd.com/engineering/information-technology/7082/a-review-on-machine-learning-techniques-for-neurological-disorders-estimation-by-analyzing-eeg-waves/vijaykumar-janga
Information Technology, Indexed Journal
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