Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.

Abstract

Background: Alzheimer's disease (AD) is a neurological disorder that affects the brain in the elderly, resulting in memory loss, mental deterioration, and loss of the ability to think and act, while being a cause of death, with its rates increasing dramatically. A popular method to detect AD is electroencephalography (EEG) signal analysis thanks to its ability to reflect neural activity, which helps to identify abnormalities associated with the disorder. Originating from its multivariate nature, EEG signals are generally handled as multidimensional time series, and the related methodology is employed. Methods: This study proposes a new transformation strategy that generates a graph representation with time resolution, which handles EEG recordings as relatively small time windows and converts these segments into a similarity graph based on signal coherence between available channels. The retrieved adjacency matrices are further flattened to form a 1-pixel image column, which represents the coherence activity from the available electrodes within the given time window. These pixel columns are concatenated horizontally for all available sliding time windows with 50% overlap, resulting in a grayscale image representation that can be input to well-known deep learning architectures specialized for images. We name this representation Connectogram-COH, a coherence-based version of the previously proposed time graph representation, Connectogram. Results: The experimental results demonstrate that the proposed Connectogram-COH representation effectively captures the coherence dynamics of multichannel EEG data and achieves high accuracy in detecting Alzheimer's disease. The time graph images serve as robust input for deep learning classifiers, outperforming traditional EEG representations in terms of classification performance. Conclusions: Connectogram-COH offers a powerful and interpretable approach for transforming EEG signals into image representations that are well suited for deep learning. The method not only improves the detection of AD but also shows promise for broader applications in EEG-based and general time series classification tasks.