Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Abstract

Abstract-Alzheimer's disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity.

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

“…Some studies report no demographics [ 6, 66, 68, 73, 74 ], only age [ 10, 75 ], only age and gender [ 61, 76, 77 ], or only age and education [ 70, 78 ]. An exception is the dataset AZTIAHORE [ 79, 80 ], which contains the youngest healthy group (20–90 years old) and a typical AD group (68–98 years old), introducing potential biases due to this imbalance. Demographic variables are established risk factors for AD [ 81 ], therefore demographics reporting is essential for this type of study.…”

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].…”

Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

“…With recent development in nonlinear analysis methods, they have been successfully applied in various fields [7][8][9][10][11][12]. Zbancioc [7] applied the Lyapunov index for the extraction of spectral coefficients of MFCC and LPCC features and achieved an emotion recognition accuracy of 75%; Firoozet al [8] evaluated nonlinear dynamic features by reconstruction of speech signals using phase space reconstruction to improve the accuracy of automatic speech recognition.…”

“…Zbancioc [7] applied the Lyapunov index for the extraction of spectral coefficients of MFCC and LPCC features and achieved an emotion recognition accuracy of 75%; Firoozet al [8] evaluated nonlinear dynamic features by reconstruction of speech signals using phase space reconstruction to improve the accuracy of automatic speech recognition. Spanish researcher Karmele Lopez applied the study of the chaotic characteristic of natural speech for the detection of Alzheimer's disease and pointed out detection of the speaker's lesions by extracting the fractal dimension features in natural speech [9,10]. Xiang and Tan of Beijing Jiaotong University combined the chaotic features from speech with other common features to detect fatigue among automobile drivers [11].…”