A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

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.