A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

Abstract

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer's disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy.These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5).Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

“…Moreover, the intention of this study was not to reach maximum classification accuracy of one particular method, but rather to show how EEG and SPECT could complement each other, while trying to render the comparison between individual and combined methods as fair as possible. However, our results are comparable with previous publications (Buscema et al, 2015 ; Gallego-Jutgla et al, 2015 ; Hatz et al, 2015 ). Other studies using entropy measures instead of measures of interaction report results with accuracies of 91.7–93.8% when discriminating MCI, AD and normal controls (McBride et al, 2015 ).…”

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

“…The aim of these studies was to investigate EEG-based biomarkers to discriminate or characterize early AD. Among these, only thirteen [ 124 , 125 , 127 , 134 , 135 , 147 – 149 , 151 , 159 , 177 – 179 ] reported follow-up information on these patients. AD conversion rate in MCI stands between 70 to 80%, whereas the rest of these patients can continue stable or convert to other dementias [ 181 ].…”

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

“…These changes in EEG data have been used as biomarkers to diagnose subjects with MCI. However, these features tend to vary across subjects, therefore have insufficient specificity (Gallego-Jutglà et al, 2015 ). One limitation is that these features only characterize abnormalities in local brain region, whereas the disorder of MCI is closely correlated with abnormal activity in multiple brain areas (Misra et al, 2009 ; Bai et al, 2012 ).…”