Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

— Mild Cognitive Impairment (MCI) is often
considered a precursor to Alzheimer’s disease (AD), with
a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of
AD and other forms of dementia. Electroencephalography
(EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial
for identifying MCI as they capture neuronal activities
and connectivity patterns linked to cognitive functions.
However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis.
To address this challenge, a novel framework has been
developed for identifying important frequency sub-bands
within EEG signals for MCI detection. In the proposed
scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time
frames. Then, four frequency sub-bands are extracted from
each segment, and spectrogram images are generated for
each sub-band as well as for the full filtered frequency
band signal segments. This process produces five different
sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually
on those image sets to perform the classification task.
Finally, the obtained results for the tested four sub-bands
are compared with the results obtained using the full
bandwidth. Our proposed framework was tested on two
MCI datasets, and the results indicate that the 16-32 Hz
sub-band range has the greatest impact on MCI detection,
followed by 4-8 Hz. Furthermore, our framework, utilizing
the full frequency band, outperformed existing state-ofthe-art methods, indicating its potential for developing
diagnostic tools for MCI detection.