Interval-based features of auditory erps for diagnosis of early alzheimer’s disease

Abstract

Introduction: It has been demonstrated that event-related potentials (ERPs) mirror the neurodegenerative process of Alzheimer’s disease (AD) and may therefore qualify as diagnostic markers. The aim of this study was to explore the potential of intervalbased features as possible ERP biomarkers for early detection of AD patients. Methods: The current results are based on 7-channel ERP recordings of 95 healthy controls (HCs) and 75 subjects with mild AD acquired during a three-stimulus auditory oddball task. To evaluate interval-based features as diagnostic biomarkers in AD, two classifiers were applied to the selected features to distinguish AD and healthy control ERPs: RBFNN (radial basis function neural network) and MLP (multilayer perceptron). Results: Using extracted features and a radial basis function neural network, a high overall diagnostic accuracy of 98.3% was achieved. Discussion: Our findings demonstrate the great promise for scalp ERP and intervalbased features as non-invasive, objective, and low-cost biomarkers for early AD detection.