ABSTRACT
This study presents a novel approach for the early detection of mild cognitive impairment (MCI) and mild Alzheimer's disease (mAD) in the elderly. Participants were 25 elderly controls (C), 25 clinically diagnosed MCI and 25 mAD patients, included after a clinical diagnosis validated by CT or MRI and cognitive tests. Our linguistic protocol involved three connected speech tasks that stimulate different memory systems, which were recorded, then analyzed linguistically by using the PRAAT software. The temporal speech-related parameters successfully differentiate MCI from mAD and C, such as speech rate, number and length of pauses, the rate of pause and signal. Parameters pauses/duration and silent pauses/duration linearly decreased among the groups, in other words, the percentage of pauses in the total duration of speech continuously grows as dementia progresses. Thus, the proposed approach may be an effective tool for screening MCI and mAD.
Subject(s)
Alzheimer Disease , Cognitive Dysfunction , Language Disorders , Aged , Alzheimer Disease/diagnosis , Cognitive Dysfunction/diagnosis , Humans , Neuropsychological Tests , SpeechABSTRACT
BACKGROUND: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. METHODS: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. RESULTS: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process - that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. CONCLUSION: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.
Subject(s)
Cognitive Dysfunction/diagnosis , Diagnosis, Computer-Assisted , Speech Recognition Software , Speech , Aged , Aged, 80 and over , Diagnosis, Computer-Assisted/methods , Female , Humans , Internet , Machine Learning , Male , Memory , Middle Aged , Models, Statistical , Neuropsychological Tests , Pattern Recognition, Automated/methods , ROC Curve , Speech Production MeasurementABSTRACT
It is known that Alzheimer's disease (AD) influences the temporal characteristics of spontaneous speech. These phonetical changes are present even in mild AD. Based on this, the question arises whether an examination based on language analysis could help the early diagnosis of AD and if so, which language and speech characteristics can identify AD in its early stage. The purpose of this article is to summarize the relation between prodromal and manifest AD and language functions and language domains. Based on our research, we are inclined to claim that AD can be more sensitively detected with the help of a linguistic analysis than with other cognitive examinations. The temporal characteristics of spontaneous speech, such as speech tempo, number of pauses in speech, and their length are sensitive detectors of the early stage of the disease, which enables an early simple linguistic screening for AD. However, knowledge about the unique features of the language problems associated with different dementia variants still has to be improved and refined.
ABSTRACT
BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) is a heterogenous syndrome considered as a prodromal state of dementia with clinical importance in the early detection of Alzheimer's Disease. We are currently developing an MCI screening instrument, the Early Mental Test (EMT) suitable to the needs of primary care physicians. The present study describes the validation process of the 6.2 version of the test. METHODS: Only subjects (n = 132, female 95, male 37) over the age of 55 (mean age 69.2 years (SD = 6.59)) scoring at least 20 points on Mini-Mental State Examination (MMSE), mean education 11.17 years (SD = 3.86) were included in the study. The psychometric evaluation consisted of Alzheimer's Disease Assessment Scale Cognitive subscale (ADAS-Cog) and the 6.2 version of EMT. The statistical analyses were carried out using the 17.00 version of SPSS statistical package. RESULTS: The optimalised cut-off point was found to be 3.45 points with corresponding 69% sensitivity, 69% specificity and 69% accuracy measures. The Cronbach-alpha, that describes the internal consistence of the test was 0.667, which is higher as compared with the same category in the case of the ADAS-Cog (0.446). A weak negative rank correlation was found between the total score of EMT 6.2 and the age of probands (rs = -0.25, p = 0.003). Similarly, only a weak correlation was found between the education levels and the total score of EMT 6.2 (rs = 0.31, p < 0.001). Two of the subtests, the repeated delayed short-time memory and the letter fluency test with a motorical distraction task had significantly better power to separate MCI and control groups than the other subtests of the EMT. CONCLUSION: The 6.2 version of EMT is a fast and simple detector of MCI with a similar sensitivity-specificity profile to the MMSE, but this version of the test definitely needs further development.
