Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

ABSTRACT

BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals. NEW METHODS: This paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. RESULTS: ResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. COMPARISON WITH EXISTING METHODS: ResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. CONCLUSION: Deep learning architectures hold great promise in enhancing the accuracy of early MCI detection.