Revolutionizing the Alzheimer’s Disease Stage Diagnosis through AI-Powered approach

Authors

  • Rageshri Bakare Research Scholar, MIT SOES, MITADT University, Pune, India
  • Virendra V. Shete Director, MIT SOES, MITADT University, Pune, India
  • Ioannis Kompatsiaris Researcher A, CERTH -ITI, Thessaloniki, Greece
  • Magda Tsolaki Professor of Neurology, MD, PhD, GAADRD, Thessaloniki, Greece

Keywords:

Alzheimer’s Disease, Dementia, EEG signal processing, Artificial Intelligence, Machine learning, Deep learning

Abstract

AI and machine learning are changing Alzheimer's disease diagnosis. These advancements are improving massive dataset analysis, enabling early diagnosis and personalized treatment. With the Continuous Wavelet Transform and Pearson's Correlation Coefficient, Electroencephalogram signal processing has become important. Machine learning classifiers enhance diagnostic accuracy. PCC-KNN, which prioritizes alpha frequency band, improves classification accuracy by combining pattern recognition and connection insights. EEG signal parameters of unhealthy and healthy patients are compared by extracting CWT and PCC parameters.  KNN, SVM, RF and DNN are trained as classifier algorithms. Using PCC to detect brain area correlations and alpha frequency oscillations helps uncover neurological disease connection problems. Combining KNN improves pattern recognition for complex alpha dynamics. KNN-PCC in the alpha frequency band improves neurological disease categorization with 96% F1-score, 95% sensitivity, 99% specificity, and 97.9% accuracy. Cognitive deterioration is linked to alpha spectrum alterations. Alpha power and slow wave activity rise may be indicated in early AD patients.

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Published

29.01.2024

How to Cite

Bakare, R. ., Shete, V. V. ., Kompatsiaris, I. ., & Tsolaki , M. . (2024). Revolutionizing the Alzheimer’s Disease Stage Diagnosis through AI-Powered approach. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 407–416. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4607

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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