Takuto Sakuma

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

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 Alzheimers 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.

Detection of mild Alzheimer's disease and mild cognitive impairment from elderly speech: Binary discrimination using logistic regression.

In this research, we have developed a novel data-mining approach for detection of cognitive impairment, SPCIR (Speech Prosody-Based Cognitive Impairment Rating), which can discriminate between mild cognitive impairment and mild Alzheimers disease from elderly using prosodic sign extracted from elderly speech during questionnaire test. This paper proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection using receiver operating characteristic (ROC) curve analysis, and reports the sensitivity and specificity of SPCIR for diagnosis (control; mild cognitive impairment/mild Alzheimers disease).

Finding Discriminative Animal Behaviors from Sequential Bio-Logging Trajectory Data

Recent advancement of bio-logging devices such as GPS sensor enables researchers in ecology to quantitatively measure animal trajectories. These animal trajectory data are often represented in the form of multi-dimensional time-series. In this paper, we develop a method for extracting interesting animal behaviors from these multi-dimensional time-series. To this end, we represent a multi-dimensional time-series as a discrete symbol sequence, and introduce some techniques developed in the context of sequential pattern mining, which has been actively studied in the literature of knowledge discovery and data mining. In animal behavior studies, it is often desired to conduct comparative studies for finding different animal behaviors in different groups, e.g, different behaviors between male and female animals etc. We use a sequential pattern mining method designed for finding so-called discriminative sequential patterns, i.e., sequential patterns that are useful for discriminating different group of animals. We apply the method to several animal trajectory datasets for demonstrating its effectiveness.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

OBJECTIVE This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimers disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawas dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subjects elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. CONCLUSION In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

Comparison of Cerebral Blood Flow Activation of Elderlies with Amnestic and Nonamnestic MCI during Verbally-Based Cognitive Test

KS3-4 認知課題遂行時脳血流のMCIサブタイプ比較分析 ○加藤 昇平、遠藤 英俊、永田 理紗子、佐久間 拓人 名古屋工業大学 大学院工学研究科 情報工学専攻、国立長寿医療研究センター Comparison of Cerebral Blood Flow Activation of Elderlies with Amnestic and Nonamnestic MCI during Verbally-Based Cognitive Test ○SHOHEI KATO1, HIDETOSHI ENDO2, RISAKO NAGATA2, TAKUTO SAKUMA1 1Dept. of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Japan, 2National Center for Geriatrics & Gerontology

Toward Personalized Cognitive Training for Elderly with Mild Cognitive Impairment: Cerebral Blood Flow Activation during Verbally-Based Cognitive Activities

This paper presents a verbally-based cognitive task for elderly with mild cognitive impairment. As designed with conscious of daily conversation, the task is done by oral answering some questionnaire. An elderly firstly talks about the topics of favorite season, travel, gourmet, and daily life, and then he/she does three cognitive tasks of reminiscence task, category recall, and working memory task. With the use of the functional near-infrared spectroscopy (fNIRS), which can measure cerebral blood flow activation non-invasively, we had collected 42 CHs fNIRS signals on frontal and right and left temporal areas from 22 elderly participants (7 males and 15 females between ages of 64 to 89) during cognitive tests in a specialized medical institute. All participates are classified into three clinical groups: elderly individuals with cognitively normal controls (CN), patients with mild cognitive impairment (MCI), and mild Alzheimers disease (AD). Toward personalized cognitive training, we report a task effect measurement by the statistical tests of fNIRS signals.