Augmented Human Research

, 5:3 | Cite as

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

  • Rushikesh Pandya
  • Shrey Nadiadwala
  • Rajvi Shah
  • Manan ShahEmail author
Original Paper


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.


Electroencephalogram (EEG) Mild cognitive impairment (MCI) Dementia Artificial intelligence (AI) K-complex Sleep spindle Neurology Alzheimer’s disease 



The authors are grateful to School of Technology, Pandit Deendayal Petroleum University and LDRP Institute of Technology and Research for the permission to publish this research.

Authors’ Contribution

All the authors made substantial contribution to this manuscript. RP, RS, SN and MS participated in drafting the manuscript. RP, RS and SN wrote the main manuscript, and all the authors discussed the results and implication on the manuscript at all stages.


Not applicable.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no competing interests.

Availability of Data and Material

All relevant data and material are presented in the main paper.

Consent for Publication

Not applicable.

Ethics Approval and Consent to Participate

Not applicable.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rushikesh Pandya
    • 1
  • Shrey Nadiadwala
    • 1
  • Rajvi Shah
    • 1
  • Manan Shah
    • 2
    Email author
  1. 1.Department of Computer EngineeringLDRP Institute of Technology and ResearchGandhinagarIndia
  2. 2.Department of Chemical Engineering, School of TechnologyPandit Deendayal Petroleum UniversityGandhinagarIndia

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