2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 2 , PP: 43-55, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Akanksha Parihar 1 * , Preety D Swami 2
Doi: https://doi.org/10.54216/FPA.140203
Dementia is a brain disorder, if not prevented; takes the form of various types of diseases that have no cure yet. Accurate classification of multiple types of dementia diseases is required to provide proper medication to the patient so that growth of that disease can be delayed. This study analyzes EEG signal for the classification of multiple dementia diseases such as Alzheimer’s disease (AD), Fronto-temporal dementia (FTD) and control normal (CN) subjects using machine learning (ML) algorithms. Each of the 19 channels of EEG dataset is analyzed separately in this work to perform the classification. Combination of parameters like Hjorth Activity, Mobility and Complexity along with kurtosis value of the data has been extracted in time-frequency domain for each EEG frequency band (Delta, Theta, Alpha, Beta and Gamma) is applied to the machine learning algorithms. This research is focused on classification of multiple dementia classes (ADvsFTD) as well as three-way (ADvsFTDvsCN) classification. This research is validated using public EEG dataset with 23 participants of each category. Best classification result is achieved using random forest classifier and leave-one-subject-out (LOSO) cross validation method. The three-way classification i.e., ADvsCNvsFTD achieved best accuracy of 75.29%, whereas binary classifications i.e. ADvsCN, ADvsFTD and CNvsFTD achieved best accuracy of 88.90%, 88.44% and 84.10% respectively. The proposed framework shows better results than existing work on dementia classification using machine learning. The results obtained from proposed framework showed that combination of EEG frequency band features can be utilized for the classification of multiple dementia diseases with greater accuracy.
Alzheimers Disease , Frontotemporal Dementia , EEG , Continuous Wavelet Transform , Time-frequency domain analysis , Feature extraction , Classification , Machine Learning , Cross Validation
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