Healthcare Informatics & Health Information Technology (SIG Health)

Paper Type

Complete

Paper Number

1595

Description

Dementia and Alzheimer's disease represent one of the biggest medical challenges of our century, manifesting the risk to individuals of losing their language or self-management skills. It is estimated that by 2050 around 1.2% of the world's population will suffer from these diseases. Since there are no effective treatment options available, it is of great importance to detect dementia tendencies at the earliest possible stage. Mild cognitive impairment represents a preclinical stage of Alzheimer's disease, allowing detecting dementia tendencies prematurely. As related work either relies on sensitive test results, does not manifest a sophisticated level of accuracy, or relies on costly techniques, further research is required. In this study, we propose a machine learning approach using resting-state EEG recordings of channels located in the Broca's area. We achieved a benchmark accuracy of 90.91% in differentiating non-MCI and MCI individuals based on the assessment of phonemic verbal fluency.

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Aug 9th, 12:00 AM

High-Performance Detection of Mild Cognitive Impairment Using Resting-State EEG Signals Located in Broca’s Area: A Machine Learning Approach

Dementia and Alzheimer's disease represent one of the biggest medical challenges of our century, manifesting the risk to individuals of losing their language or self-management skills. It is estimated that by 2050 around 1.2% of the world's population will suffer from these diseases. Since there are no effective treatment options available, it is of great importance to detect dementia tendencies at the earliest possible stage. Mild cognitive impairment represents a preclinical stage of Alzheimer's disease, allowing detecting dementia tendencies prematurely. As related work either relies on sensitive test results, does not manifest a sophisticated level of accuracy, or relies on costly techniques, further research is required. In this study, we propose a machine learning approach using resting-state EEG recordings of channels located in the Broca's area. We achieved a benchmark accuracy of 90.91% in differentiating non-MCI and MCI individuals based on the assessment of phonemic verbal fluency.

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