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This manuscript proposes a multi-class recognition system with improved classification accuracy using machine-learning techniques by considering electroencephalogram (EEG) and speech signal analysis to classify and detect patients affected by Alzheimer’s disease (AD) and Parkinson’s disease (PD) in its initial stages. Acquired raw EEG and speech signal are pre-processed utilising wavelet filters to eliminate noises, then the features impedivity, phase angle, higher frequency slope of phase angle, standard deviation, minimal pitch, maximal pitch, count of voice breaks are extracted from EEG and speech signal using bag of deep reduced features. Optimal features mean absolute values, enhanced wavelength, wavelength, zeros crossing are selected using an improved chaotic multi-verse Harris Hawks optimisation (CMVHHO) algorithm. Finally, Random Forest (RF) classifier is employed to classify patients suffered by AD with PD. Experimental results show 95.17%, 96.31% and 97.48% higher accuracy for AD compared with existing methods, like MCR-AD-PD-SPWVD-CNN, MCR-AD-PD-ICA-DSCHN and MCR-AD-DWT-KNN-RLDA, respectively.

computer methods in biomechanics and biomedical engineering: imaging and visualization

Multi-class Recognition of Alzheimer’s and Parkinson’s diseases using Bag of Deep reduced Features (BoDrF) with Improved Chaotic Multi Verse Harris Hawks Optimization (CMVHHO) and Random Forest (RF) based classification for early diagnosis