Title: Auditory model system to recognise Alzheimer's diseases: speech signal analysis

Authors: Ravi Kumar; R. Prabha; B. Kannan; T.J. Nagalakshmi; Sheshang D. Degadwala

Addresses: Jaypee University of Engineering and Technology, Madhya Pradesh 473226, India ' Department of Electronics and Communication Engineering, Sri Sairam Institute of Technology, Chennai, India ' Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, India ' Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India ' Department of Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India

Abstract: Alzheimer's disease is a catch-all phrase for a variety of illnesses, including numerous neurodegenerative disorders. A century ago, neurosyphilis-caused dementia was the most frequent cause of dementia in developed nations, but Alzheimer's disease (AD) is today the most common cause. Dementia might still be difficult to diagnose because of a number of barriers. Early symptoms overlap with other disorders, and the potential of several, or mixed, aetiologies are just a few of the factors that contribute to a wide range of possible outcomes. Because language diminishes along with neurodegeneration in Alzheimer's disease, it is a significant source of clinical information. As a result, data on speech and language have been intensively investigated in relation to its diagnosis. Text characteristics extracted from the transcripts are used to detect AD using an SVM classifier. However, the accuracy of automatic assessment falls as WER rises, the two are very weakly associated (0.31).

Keywords: Alzheimer's disease; auditory model system; wavelet transform; gammatone filter.

DOI: 10.1504/IJMEI.2024.141794

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.6, pp.560 - 570

Received: 12 Mar 2022
Accepted: 15 Jun 2022
Published online: 02 Oct 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article