A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification - Prabhakar, Sunil Kumar; Rajaguru, Harikumar; Kim, Chulho; Won, Dong-Ok (Frontiers in Human Neuroscience, 2022)
The vital data about the electrical activities of the brain are contained by the Electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface is measured by EEG. One of the most important aspect in the field of neuroscience and neural engineering is EEG signal analysis as it aids greatly in dealing with the commercial applications too. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning gives us good results. In this work, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model and then for feature selection, a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets and finally a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.