Advanced International Journal for Research

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Volume 7 Issue 2
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EEG Based Analysis for Early Cognitive Impairment Detection Using Hybrid Deep Learning Model

Author(s) Dr. Yogesh S. Khandekar
Country India
Abstract Cognitive impairment is a cause of concern for over 19% of adults aged 50 and above but only less than 8% are diagnosed on time in primary care settings. Presently, the diagnosis is dependent on long neuropsychological tests, evaluations by specialists, and subjective assessments—this makes it difficult for patients to get early intervention when disease- modifying treatments are most effective. We propose a deep learning solution, attention-augmented CNN-LSTM, to solve this problem, which can quickly and non-invasively screen cognitive impairment from EEG. In essence, our hybrid model intends to capture not only the spatial patterns (using CNN) of brain activity but also the temporal changes (using LSTM) while at the same time, ensuring clinical interpretability through attention mechanisms. In order for our model to learn robustly, we have employed thorough signal preprocessing (Savitzky-Golay and Butterworth filtering), class balancing using SMOTE, and adaptive training (EarlyStopping and ReduceLROnPlateau) techniques. By integrating the attention module, our method offers diagnostic transparency by showing which EEG temporal segments have the most impact on the prediction of the disorder— this is very important for clinical implementation. These findings are far beyond traditional deep learning baselines (literature: 75– 90%) and offer a transparent, trustworthy, and convenient way to the clinical early cognitive impairment evaluation that can be carried out in clinics.
Keywords EEG, Early Detection, Cognitive impairment, CNN-LSTM, Attention Mechanism, Signal Preprocessing, SMOTE and Deep Learning.
Field Engineering
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-09
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E-ISSN 3048-7641
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