Title:A Machine Learning Prediction Model for Automated Brain Abnormalities Detection
Volume: 11
Issue: 1
Author(s): Satyajit Anand*, Sandeep Jaiswal and Pradip Kumar Ghosh
Affiliation:
- Department of ECE, School of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh,India
Keywords:
EEG, denoising, filter, brain abnormalities, artificial neural network, orthogonal wavelet.
Abstract: Background: The rapid improvement in technology enables an Electroencephalogram
(EEG) to detect a diverse range of brain disorders easily. The design of sophisticated signal processing
methods for an efficient analysis of the EEG signals is exceptionally essential. Raw EEG
signal is contaminated by noise and artefacts that modify the spectral-spatial and temporal information
of the signal and renders inaccurate clinical interpretation. Denoising of the signal is the first
step to refine the signal quality and identify patient's mental state from the signal although it is not
an easy task because of high dimensionality and complexity of EEG signal. The present study highlights
three conditions of the brain namely stroke, brain death, and a healthy state. The primary concern
is to detect the most abnormal conditions of the brain, i.e., an EEG with a critical stage.
Method: This paper introduces a neoteric technique for the analysis of EEG signals of the three conditions
using filters such as Fuzzy filter and wavelet orthogonal filter to obtain highly accurate resultant
signals. Further, the resultant filter is trained in Neural Network for predicting the brain abnormalities.
The proposed system is found to be efficient in denoising the EEG waves.
Results: The result shows that the classification accuracy of multiclass EEG dataset achieved and
the performance of ANN is high and it was found to be the best validation performance of ANN
which is 0.2303.
Conclusion: This paper comprehensively describes the denoising of the EEG signals that will provide
accuracy in the diagnosis of the EEG to detect brain disorders. The Fuzzy filter pre-processes
the signals by considering the noisy signal by an ideal value in such a way that the desired metric
(the filtered output) is reduced. The orthogonal wavelet filter produces a single scaling function and
wavelet function. The EEG features are extracted from multiple-level decompositions of EEGs by
DWT. Finally, the features are classified using Back propagation artificial neural network that categorizes
the EEGs to make the diagnosis easier for the brain abnormalities.
