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

• Main Authors: Md. Nurul Ahad Tawhid (Author), Siuly Siuly (Author), Enamul Kabir (Author), Yan Li (Author)
• Format: Book
• Published: IEEE, 2024-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2023.3347032
• Keywords: CNN; deep learning; electroencephalogram (EEG); frequency sub-band; mild cognitive impairment (MCI); spectrogram; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 189-199 (2024)

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-of-the-art methods, indicating its potential for developing diagnostic tools for MCI detection.