A hybrid feature selection approach for the early diagnosis of Alzheimer's disease
| Autor: | François-Benoît Vialatte, Justin Dauwels, Mohamed Elgendi, Esteve Gallego-Jutglà, Jordi Solé-Casals, Andrzej Cichocki |
|---|---|
| Přispěvatelé: | Universitat de Vic. Escola Politècnica Superior |
| Rok vydání: | 2015 |
| Předmět: |
Computer science
Biomedical Engineering Relative power Feature selection Disease Electroencephalography Machine learning computer.software_genre Sensitivity and Specificity Pattern Recognition Automated Diagnosis Differential Cellular and Molecular Neuroscience Alzheimer Disease medicine Humans Cognitive Dysfunction Diagnosis Computer-Assisted Aged medicine.diagnostic_test business.industry Brain Reproducibility of Results Pattern recognition Data set Alzheimer Malaltia d' Feature (computer vision) Pattern recognition (psychology) Selection method Artificial intelligence business computer Algorithms |
| Zdroj: | RIUVic. Repositorio Institucional de la Universidad de Vic instname Recercat. Dipósit de la Recerca de Catalunya |
| Popis: | Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG). However, choosing suitable measures is a challenging task. Among other measures, frequency relative power (RP) 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 RP on EEG signals, examining the changes found in different frequency ranges. Approach .W efirst explore the use of a single feature for computing the classification rate (CR), 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 mild cognitive impairment (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 CRs 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 CR of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using four 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. |
| Databáze: | OpenAIRE |
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