Automatic classification of Alzheimer’s disease through features extracted from speech recordings
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1254Keywords:
Speech Processing, Deep Learning, Alzheimer's diseaseAbstract
Alzheimer's disease is a progressive neurodegenerative pathology and is among the most common forms of dementia in older people. Changes in memory are common symptoms, and changes in speech and language can be signs of cognitive decline. Intelligent systems have the potential for use as diagnostic support tools. Objective: To propose a Convolutional Neural Network model for classifying Alzheimer's disease using features extracted from speech recordings. Method: We used speech segments with and without pauses from healthy individuals and those with Alzheimer's disease to extract features and recognize patterns in spectrograms. Model training uses a 5-fold stratified cross-validation method. Results: The results showed accuracy, sensitivity, and specificity metrics of 97.37%, 97.04%, and 97.62%, respectively. Conclusion: The proposed model presented promising results and could contribute to studying non-invasive biomarkers that detect Alzheimer's disease early.
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