The shape of dementia: new measures of morphological complexity in event-related potentials (ERP) and its application to the detection of Alzheimer’s disease

Abstract

In this paper, we address the problem of quantifying the commonly observed disorganization of the stereotyped wave form of the ERP associated with the P300 component in patients with Alzheimer's disease. To that extent, we propose two new measures of complexity which relate the spectral content of the signal with its temporal waveform: the spectral matching coefficient and the spectral matching entropy. We show by means of experiments that those measures effectively measure complexity and are related to the shape in an intuitive way. Those indexes are compared with commonly used measures of complexity when comparing AD patients against age-matched healthy controls. The results indicate that AD ERP signals are, indeed, more complex in the shape than that of controls, and this result is evidenced mainly by means of our new measures which have a better performance compared to similar ones. Finally, we try to explain this increase in complexity in light of the communication through coherence hypothesis framework, relating commonly found changes in the EEG with our own results.

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

“…Among previous studies that have explored various ERP features for the discrimination of AD from normal individuals, Jimenez‐Rodriguez et al 31 . extracted SMC and SSME features and reached a sensibility of 81% and a specificity of 85%.…”

“…In an effort to develop a diagnostic procedure that discriminates between AD and age‐matched control individuals, Jimenez‐Rodriguez et al 31 . proposed two novel measures of complexity of the shape in time series: spectral matching complexity (SMC) and spectral matching entropy (SSME).…”

“…In an effort to develop a diagnostic procedure that discriminates between AD and age-matched control individuals, Jimenez-Rodriguez et al 31 The objective of this study was to assess the viability of an automated classification approach. We used interval-based features 34 extracted from cognitive ERP recordings of brain electrical activity during an oddball paradigm to distinguish AD patients from HCs.…”

Non-Aβ-Dependent Factors Associated with Global Cognitive and Physical Function in Alzheimer’s Disease: A Pilot Multivariate Analysis

“…Considerably important is the fact that individuals with MCI have an increased risk of developing Alzheimer’s disease (AD) [2,4]. AD is the most common form of dementia, characterized by a progressive deterioration of cognitive function due to widespread neuronal loss and synaptic dysfunction, accompanied by extracellular amyloidosis and intracellular tangle formation [5]. Current literature suggests that patients with MCI progress to AD at a rate of 10% to 15% per year, and 80% of these patients converted to AD after approximately 6 years of follow-up [2].…”

“…In recent decades, much effort has been dedicated to the early clinical diagnosis of dementing disorders with the aim of identifying factors associated with AD [6]. Most of the studies have mainly focused on the association of cognitive decline with biomarkers obtained by neuroimaging and from cerebrospinal fluid [4,5,7,8]. However, it has been demonstrated that cognitive deterioration, accumulation of amyloid-β (Aβ) and tau protein are not equivalent and not linear with respect to disease progression and the appearance of its cognitive symptoms [9].…”