EEG Analysis Using Bio-Inspired Metaheuristic Approach

• Published in: Evolving Role of AI and IoMT in the Healthcare Market pp. 33 - 45
• Main Authors: Yedurkar, Dhanalekshmi P., Metkar, Shilpa P.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Subjects: Alzheimer’s disease; Ant colony optimization; Autism; Bio-inspired; Complex computing; Cuckoo search; Early diagnosis; EEG analysis; Epilepsy; Flower pollination algorithm; Genetic algorithm; Grey wolf optimization; Metaheuristic; Neurological diseases; Parkinson’s disease; Particle swarm optimization
• ISBN: 3030820785; 9783030820787
• DOI: 10.1007/978-3-030-82079-4_2

Currently, neurological signals are used in various scientific fields which include Brain–Computer Interfaces (BCI), Cognitive Science, Medical Science and Neuroscience. Electroencephalography (EEG) signals are used to examine and note the activities that go on inside the brain. The detection of various diseases such as epilepsy, Alzheimer’s disease (AD), autism and Parkinson’s disease (PD) is possible by accurate analysis of an EEG signal. Early diagnosis of these diseases is essential as it helps the patients to take preventive measures. EEG signal analysis is difficult because the nature of the signal is complex and the signal may also include noise, small sample size and high dimensionality. Therefore, a very demanding process that needs a detailed analysis of the entire length of the EEG data is required. Algorithms based on artificial intelligence are not very effective in this field because of their dependency on high precision data as well computations that are very complex. In order to overcome these problems faced by the conventional algorithms, newer trends lean towards using Bio-Inspired (BI) metaheuristic algorithms which show the promise of a technique to solve complicated optimization problems. A review of the different BI algorithms that are brought into use for early detection of multiple brain diseases is the main focus of this chapter.