EEG Patterns in Mild Cognitive Impairment Patients
Author Information
Author(s): Mary Baker, Kwaku Akrofi, Randolph Schiffer, Michael W. O’Boyle
Primary Institution: Texas Tech University
Hypothesis
Can EEG patterns help classify MCI patients and predict their progression to Alzheimer's disease?
Conclusion
The study suggests that EEG pattern recognition may effectively identify MCI patients at risk of progressing to Alzheimer's disease.
Supporting Evidence
- EEG patterns can classify AD patients with over 80% accuracy.
- 4 out of 6 MCI patients' clinical statuses were correctly predicted at follow-up.
- EEG power differences were found between MCI patients and controls.
Takeaway
Doctors can use brain wave patterns to tell which patients with mild memory problems might get worse and develop Alzheimer's disease.
Methodology
EEG data were recorded from participants and analyzed using a k-means clustering algorithm to classify MCI patients.
Potential Biases
Potential bias due to the small number of participants and the exclusion of some patients.
Limitations
The study is preliminary and based on a small sample size.
Participant Demographics
17 AD patients, 25 MCI patients, and 16 age-matched controls, mostly right-handed.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.03
Digital Object Identifier (DOI)
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