Mild cognitive impairment detection with optimally selected EEG channels based on variational mode decomposition and supervised machine learning

Aljalal, Majid; Molinas, Marta; Aldosari, Saeed A.; AlSharabi, Khalil; Abdurraqeeb, Akram M.; Alturki, Fahd A.

doi:10.1016/j.bspc.2023.105462

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…Using longer segmentation lengths may produce misleading results [32] due to increased non-stationarity. Also, utilizing longer segmentation lengths typically results in an elevated computational cost [34,35]. Numerous studies are conducted on a single neurological disorder with various segmentation lengths empirically as 1 s, 2 s, 3 s, and 5 s [16,31,[35][36][37][38].…”

Section: Previous Workmentioning

confidence: 99%

“…Also, utilizing longer segmentation lengths typically results in an elevated computational cost [34,35]. Numerous studies are conducted on a single neurological disorder with various segmentation lengths empirically as 1 s, 2 s, 3 s, and 5 s [16,31,[35][36][37][38]. In this study, we examined two segmentation lengths (1 s and 2 s) to identify neurological disabilities by comparing their features, classification performance results, and visual representations.…”

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization

Ji,

Jayarathna,

Perrotti

et al. 2023

Applied Sciences

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Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization

Ji,

Jayarathna,

Perrotti

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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Diagnose Alzheimer’s disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals

Rezaee,

Zhu

2025

Biomedical Signal Processing and Control

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Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy

Aljalal,

Aldosari,

Molinas

et al. 2024

Sci Rep

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Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.

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Mild cognitive impairment detection with optimally selected EEG channels based on variational mode decomposition and supervised machine learning

Cited by 7 publications

References 48 publications

Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization

Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization

Diagnose Alzheimer’s disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy

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