Abstract
Alzheimer's disease (AD) ranks among the main types of neurodegenerative disorders. Patients suffering AD should tackle serious problems since their language skills malfunction. The impact of such disorders is reflected by reduced quality and feature variation of spontaneous speech signals in speech analysis. This paper aims at assessing the variations of some specific types of these energy- and entropy-based features within the frequency range of the speech signals. In the approach followed, the wavelet-packet coefficients are utilized to extract the energy and entropy measures at every spectral sub-band in six successive levels of decomposition. However, the decomposition process conducts a set of high-dimensional feature vectors that is a challenging task for feature selection. This study suggests the application of a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for enhancing a group of the sub-band indexes of a wavelet-packet for which the extracted features lead to the highest diagnosis rate of the grouping of Alzheimer's and healthy individuals. The technique proposed here showed that the best overall classification results for both optimized entropy feature vs. energy are more noticeable in discriminating patients with AD from healthy subjects. It is also confirmed the significant impact of multi-objective feature selection on performance of classification (i.e., disease diagnosis) and, its conformity to the disordered nature of the biological signals could help diagnose AD in an efficient manner.
Highlights
It is believed that patients will be detected quicker for more clinical trials to detect dementia earlier by developing noninvasive intelligent methods
This study aimed to evaluate the ability of energy and entropy features to describe the origin of the loss of language skills, which is reflected in both difficulties in speaking and comprehension in Alzheimer’s disease (AD)
The energy and entropy features were extracted for six successive levels of waveletpacket decomposition
Summary
It is believed that patients will be detected quicker for more clinical trials to detect dementia earlier by developing noninvasive intelligent methods. Having improved systems with present objective analysis for automatic grouping and diagnosis of dementia could pave the way for visiting the patients in a timely manner for upcoming medical and financial choices. These techniques do not change or hamper the patients’ abilities, since the spontaneous speech in these techniques is not supposed to be a stressful test by the patient. The loss of languagerelated communication ability by Alzheimer is dependent on the stage of the disease These speech deficits can be separated into three stages: pre-clinical stage, intermediate stage, and advanced stage.
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