Open AccessInterval‐based features of auditory ERPs for diagnosis of early Alzheimer's disease
Author(s) -
Sabbaghi Neda,
Sheikhani Ali,
Noroozian Maryam,
Sabbaghi Navide
Publication year - 2021
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1002/dad2.12191
Subject(s) - event related potential , electroencephalography , multilayer perceptron , oddball paradigm , audiology , interval (graph theory) , stimulus (psychology) , scalp , computer science , artificial intelligence , pattern recognition (psychology) , psychology , artificial neural network , speech recognition , neuroscience , medicine , cognitive psychology , mathematics , combinatorics , anatomy
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.