Buildout of Methodology for Meticulous Diagnosis of K-Complex in EEG for Aiding the Detection of Alzheimer’s by Artificial Intelligence

Application of artificial intelligence (AI) in health-care detection is a domain of exceptional research and interest in today’s world. And hence among this domain, a considerable inclination is toward creating a smart system that is AI for aiding identification of brain-related disease—Alzheimer’s—using electroencephalogram (EEG). Certain AI-based techniques as well as systems have been created for EEG examination and interpretation, but they have a common drawback that is lack of shrewdness and acuteness. Therefore, to overcome these drawbacks, a different methodology or technique is suggested in this paper which is able to mold the AI technique for better EEG Cz strip K-complex identification. This suggested method and structure of AI detection system is relied on quantitative scrutinization of Cz strip and embedding-established EEG explication principles for detection of K-complex and Alzheimer’s. This technique unconditionally relied on facts and information of neuroscience that are applied by expert in health care such as neurologist to create a detailed review of sick person’s EEG. The suggested technique also allots a potential of learning on its own to the AI so that it can apply the events in future examinations.