Early detection of cognitive impairment in the elderly based on Bayesian mining using speech prosody and cerebral...

With the aim of providing computer aided diagnosis of dementia, we have developed a non-invasive screening system of the elderly with cognitive impairment. In our previous research, we have studied two data-mining approaches by focusing on speech-prosody and cerebral blood flow (CBF) activation during cognitive tests. On the power of these research results, this paper presents a prosody-CBF hybrid screening system of the elderly with cognitive impairment based on a Bayesian approach. The system is constructed by SPCIR (Speech Prosody-Based Cognitive Impairment Rating) based cutoff as the 1st screening, and, as the 2nd screening, two-phase Bayesian classifier for discriminating among elderly individuals with three clinical groups: elderly individuals with normal cognitive abilities (NC), patients with mild cognitive impairment (MCI), and Alzheimer's disease (AD). This paper also reports the screening examination and discusses the cost-effectiveness and the discrimination performance of the proposed system for early detection of cognitive impairment in elderly subjects.