Key result
A novel CNN-based framework utilizing spectrogram images of full frequency band (0.5-32 Hz) EEG signals achieved 99.03% accuracy for MCI detection, with the 16-32 Hz (beta) sub-band identified as the most impactful.
Why the study?
Traditional approaches struggle to identify precise frequency band-based EEG biomarkers for the accurate and timely diagnosis of mild cognitive impairment.
Does a CNN-based framework using specific EEG frequency sub-bands accurately detect Mild Cognitive Impairment compared to using the full frequency band?
Does a CNN-based framework using specific EEG frequency sub-bands accurately detect Mild Cognitive Impairment compared to using the full frequency band?
A deep learning framework utilizing spectrogram images of EEG signals demonstrated that the 16-32 Hz (beta) sub-band is highly effective for detecting Mild Cognitive Impairment, though the full 0.5-32 Hz band yielded the highest overall diagnostic accuracy.
May aid timely MCI detection via EEG; leaves open validation before clinical adoption.
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.
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Tawhid et al. (2023) studied Mild Cognitive Impairment (n=136). Spectrogram image-based CNN framework using full frequency band (0.5-32 Hz) EEG signals vs. Other frequency sub-bands and existing state-of-the-art methods was evaluated on Classification accuracy for MCI detection. A novel CNN-based framework utilizing spectrogram images of full frequency band (0.5-32 Hz) EEG signals achieved 99.03% accuracy for MCI detection, with the 16-32 Hz (beta) sub-band identified as the most impactful.
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