On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

Abstract

Abstract:Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer's disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer's disease patients.

Population Aging in the European Information Societies: Towards a Comprehensive Research Agenda in eHealth Innovations for Elderly

“…Therefore, biometrics should be addressed from a global point of view taking into account the natural process of ageing in order to adjust the technological solution that can help in early detection of cognitive impairments. One such example is the research conducted by López-de-Ipiña et al [45,46,47,48] on improving the early diagnose of Alzheimer disease (AD) using continuous speech signal.…”

“…By analysing continuous speech, the system calculates an index whose values show whether the subject can be classified as being affected by AD. Emotional Temperature (ET) is another parameter that, combined with other traditional speech parameters, can improve and facilitate the early diagnosis of AD [46,47]. Recently, other nonlinear parameters like fractal dimension have demonstrated their validity as biomarkers to aid for AD early diagnosis [48].…”

Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts

“…Second, the lexical frequency of words and part-of-speech tags may also change significantly as the patient has problems with finding words (Croot et al, 2000). Third, the emotional responsiveness of the patient was also observed to change in many cases (Lopez-de Ipiña et al, 2015).…”

Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

“…Features for specific and general cohort studies owing to identify AD spectrum come from various sources: cognitive, genetic, neuroimaging, proteomic and others [16]. These features intend to provide insights on biological factors AD which is critical to understanding the disease progression and to early prevention strategies [1].…”

“…Interesting applications of feature selection techniques contributed to the understanding of AD, like the construction of potential biomarkers for enhanced classification. For instance, Lopez-de-Ipiña et al owing to determine preclinical biomarkers for AD apply feature selection techniques on spontaneous speech to extract discriminant features [16]. They also were able to correctly classify AD subjects using kNN and multilayer perceptron (MLP) classifiers obtaining accuracy of 87.30% and 90.90%, respectively to each classifier.…”