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Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based
Reza Akbari Movahed1, Mohammadreza Rezaeian1
1Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran.
Abstract:
Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer's and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.
Insights
This study introduces a machine learning framework using electroencephalography (EEG) to accurately diagnose mild cognitive impairment (MCI). The system achieved high accuracy, offering a potential tool for early detection of dementia.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Diagnostics
Background:
- Mild cognitive impairment (MCI) diagnosis is challenging due to overlapping symptoms with normal aging.
- Early detection of MCI is crucial for preventing progression to Alzheimer's disease and other dementias.
- Machine learning (ML) applied to electroencephalography (EEG) shows promise for early MCI diagnosis.
Purpose of the Study:
- To develop and validate an ML framework for accurate and early diagnosis of MCI using EEG signals.
- To identify optimal feature extraction and selection methods for MCI classification.
- To evaluate the performance of the proposed framework against existing methods.
Main Methods:
- Extraction of spectral, functional connectivity, and nonlinear features from EEG data.
- Application of Sequential Backward Feature Selection (SBFS) for optimal feature subset identification.
- Evaluation of various ML classifiers, including Linear Support Vector Machine (LSVM), using a 10-fold cross-validation approach.
Main Results:
- The proposed framework achieved high diagnostic performance: 99.4% accuracy, 98.8% sensitivity, 100% specificity, and 99.4% F1-score.
- The best performance was obtained using LSVM with a combination of all extracted feature sets.
- The system demonstrated robust performance in distinguishing MCI subjects from normal controls.
Conclusions:
- The developed ML framework provides accurate and robust MCI detection using EEG.
- The system outperforms previous approaches and has potential for clinical application as a computer-aided diagnosis (CAD) tool.
- Early and accurate MCI diagnosis can aid in timely intervention and management of dementia progression.
