Interval-based features of auditory ERPs for diagnosis of early Alzheimer's disease

Neda Sabbaghi; Ali Sheikhani; Maryam Noroozian; Navide Sabbaghi

doi:10.1002/dad2.12191

Interval-based features of auditory ERPs for diagnosis of early Alzheimer's disease

Alzheimers Dement (Amst). 2021 May 18;13(1):e12191. doi: 10.1002/dad2.12191. eCollection 2021.

Authors

Neda Sabbaghi¹, Ali Sheikhani¹, Maryam Noroozian², Navide Sabbaghi¹

Affiliations

¹ Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran.
² Department of Psychiatry School of Medicine Tehran University of Medical Sciences Tehran Iran.

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 interval-based 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 interval-based features as non-invasive, objective, and low-cost biomarkers for early AD detection.

Keywords: Alzheimer's disease; artificial neural network; event‐related potential; interval‐based features; multilayer perceptron; radial basis function neural network.