Key result
The DWT-CNN model achieved 100% accuracy in classifying Alzheimer's disease patients from healthy controls using raw EEG alpha band data under both eyes-open and eyes-closed conditions.
Why the study?
Automated systems using non-invasive, cost-effective EEG signals can determine dementia stages, motivating the design of a practical AD detection tool.
Does a DWT-CNN model using raw EEG time series accurately detect Alzheimer's Disease compared to healthy controls?
Cross-Sectional (n=48)
Does a DWT-CNN model using raw EEG time series accurately detect Alzheimer's Disease compared to healthy controls?
Effect estimate: 100% accuracy
A DWT-CNN model using raw EEG time series can accurately distinguish Alzheimer's disease patients from healthy controls, with the eyes open state improving classification rates.
Should not yet inform Alzheimer's diagnosis; leaves open prospective validation in larger, diverse cohorts.
Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients’ life. Alzheimer’s Disease (AD) is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC (12 eyes open (EO), 12 eyes closed (EC)), and 24 AD (HC (12 eyes open (EO), 12 eyes closed (EC)). The aim of current study is to design a practical AD detection tool for AD/HC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform (DWT) to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities (2D TF image conversion, or MRI) in a multimodal approach.
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Şeker et al. (2022) conducted a cross-sectional in Alzheimer's Disease (n=48). DWT-CNN model for EEG analysis vs. Healthy controls was evaluated on Classification accuracy of Alzheimer's Disease versus Healthy Controls (100% accuracy). The DWT-CNN model achieved 100% accuracy in classifying Alzheimer's disease patients from healthy controls using raw EEG alpha band data under both eyes-open and eyes-closed conditions.
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