Mild Cognitive Impairment Detection with Machine Learning and Topological Data Analysis Applied to EEG Time-series in...

We report a novel approach to dementia neurobiomarker development from EEG time series using topological data analysis (TDA) methodology and machine learning (ML) tools in the 'AI for social good' application domain, with possible following application to home-based point of care diagnostics and cognitive intervention monitoring. We propose a new approach to a digital dementia neurobiomarker for early-onset mild cognitive impairment (MCI) prognosis. We report the best median accuracies in a range of upper 85% linear discriminant analysis (LDA), as well above 90% for linear SVM and deep fully connected neural network classifier models in leave-one-out-subject cross-validation, which presents very encouraging results in a binary healthy cognitive aging versus MCI stages using TDA features applied to brainwave time series patterns captured from a four-channel EEG wearable.Clinical relevance- The reported study offers an objective dementia early onset neurobiomarker prospect to replace traditional subjective paper and pencil tests with an application of EEG-wearable-based and topological data analysis machine learning tools in a possibly successive home-based point-of-care environment.