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Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis
Saleha Khatun1, Bashir I Morshed2, Gavin M Bidelman3
1Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN 38152, USA.
Abstract:
A deviation in the soundness of cognitive health is known as mild cognitive impairment (MCI), and it is important to monitor it early to prevent complicated diseases such as dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Traditionally, MCI severity is monitored with manual scoring using the Montreal Cognitive Assessment (MoCA). In this study, we propose a new MCI severity monitoring algorithm with regression analysis of extracted features of single-channel electro-encephalography (EEG) data by automatically generating severity scores equivalent to MoCA scores. We evaluated both multi-trial and single-trail analysis for the algorithm development. For multi-trial analysis, 590 features were extracted from the prominent event-related potential (ERP) points and corresponding time domain characteristics, and we utilized the lasso regression technique to select the best feature set. The 13 best features were used in the classical regression techniques: multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR). The best results were observed for ER with an RMSE of 1.6 and residual analysis. In single-trial analysis, we extracted a time-frequency plot image from each trial and fed it as an input to the constructed convolutional deep neural network (CNN). This deep CNN model resulted an RMSE of 2.76. To our knowledge, this is the first attempt to generate automated scores for MCI severity equivalent to MoCA from single-channel EEG data with multi-trial and single data.
Insights
This study introduces an automated algorithm using electro-encephalography (EEG) to assess mild cognitive impairment (MCI) severity, offering an alternative to manual scoring. The novel method accurately generates scores comparable to the MoCA, aiding early detection of cognitive decline.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Informatics
Background:
- Mild cognitive impairment (MCI) is a decline in cognitive health, necessitating early monitoring to prevent progression to dementia, Alzheimer's disease (AD), and Parkinson's disease (PD).
- Current MCI severity assessment relies on manual scoring using the Montreal Cognitive Assessment (MoCA), which can be time-consuming and subjective.
Purpose of the Study:
- To develop and evaluate a novel algorithm for automated MCI severity scoring using single-channel electro-encephalography (EEG) data.
- To generate automated severity scores equivalent to MoCA scores, enabling more efficient and objective monitoring of cognitive health.
Main Methods:
- Feature extraction from event-related potential (ERP) points and time-domain characteristics of multi-trial EEG data.
- Application of lasso regression for feature selection, followed by multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR).
- Development of a convolutional deep neural network (CNN) model for single-trial analysis using time-frequency plot images.
Main Results:
- Ensemble regression (ER) achieved the best performance in multi-trial analysis with a Root Mean Square Error (RMSE) of 1.6.
- The single-trial CNN model demonstrated an RMSE of 2.76.
- This research represents the first attempt to automate MoCA-equivalent MCI severity scoring from single-channel EEG data using both multi-trial and single-trial analyses.
Conclusions:
- The proposed automated algorithm effectively estimates MCI severity using single-channel EEG data.
- The multi-trial analysis with ensemble regression shows promising results for objective and efficient MCI monitoring.
- This approach offers a potential advancement in the early detection and management of cognitive impairments.
