• Nilesh N Kulkarni
• Saurabh V Parhad

Previous studies have highlighted that EEG signal of Alzheimer’s disease patients tends to be less complex and have low synchronization as compared to that of healthy and normal subjects. These changes in EEG signals of Alzheimer’s disease patients start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Combining these features, healthy subjects and Alzheimer’s disease patients are classified by use of different machine learning algorithms such as Support Vector Machine, Deep Learning and Naive Bayes Classifier. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.

Alzheimer’s disease, Dementia, EEG, Complexity features, Support Vector Machine Classifier

"Diagnosis of Alzheimer’s disease by means of Wavelet & Synchrony based features & Machine Learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.173-177, May 2019, Available :http://www.jetir.org/papers/JETIRCS06039.pdf

"Diagnosis of Alzheimer’s disease by means of Wavelet & Synchrony based features & Machine Learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp173-177, May 2019, Available at : http://www.jetir.org/papers/JETIRCS06039.pdf