Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for large-scale assessments. MCI disfluencies produce non-grammatical speech that requires manual or high precision automatic correction of transcripts. In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments. The network measurements were applied with well-known classifiers to automatically identify MCI in transcripts, in a binary classification task. A comparison was made with the performance of traditional approaches using Bag of Words (BoW) and linguistic features for three datasets: DementiaBank in English, and Cinderella and Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using only complex networks, while Support Vector Machine was superior to other classifiers. CNE provided the highest accuracies for DementiaBank and Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably owing to its short narratives. The approach using linguistic features yielded higher accuracy if the transcriptions of the Cinderella dataset were manually revised. Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments
Detection of memory impairment has been based on linguistic, acoustic, and demographic features, in addition to scores of neuropsychological tests. Linguistic and acoustic features were used to automatically detect aphasia @cite_11 ; and AD @cite_10 or dementia @cite_2 in the public corpora of DementiaBank talkbank.org DementiaBank . Other studies distinguished different types of dementia @cite_8 @cite_0 , in which speech samples were elicited using the Picnic picture of the Western Aphasia Battery @cite_13 . also used the Picnic scene to detect MCI, where the subjects were asked to write (by hand) a detailed description of the scene.
@cite_13 @cite_8 @cite_0 @cite_2 @cite_10 @cite_11
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Although memory impairment is the main symptom of Alzheimer's disease (AD), language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques.We aim to demonstrate state-of-the-art accuracy in automatically identifying Alzheimer's disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis.Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with "possible" or "probable" AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors.We obtain state-of-the-art classification accuracies of over 81 in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment.Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD. This pilot study evaluates the ability of machined learned algorithms to assist with the differential diagnosis of dementia subtypes based on brief (< 10 min) spontaneous speech samples. We analyzed 1 recordings of a brief spontaneous speech sample from 48 participants from 5 different groups: 4 types of dementia plus healthy controls. Recordings were analyzed using a speech recognition system optimized for speakerindependent spontaneous speech. Lexical and acoustic features were automatically extracted. The resulting feature profiles were used as input to a machine learning system that was trained to identify the diagnosis assigned to each research participant. Between groups lexical and acoustic differences features were detected in accordance with expectations from prior research literature suggesting that classifications were based on features consistent with human-observed symptomatology. Machine learning algorithms were able to identify participants' diagnostic group with accuracy comparable to existing diagnostic methods in use today. Results suggest this clinical speech analytic approach offers promise as an additional, objective and easily obtained source of diagnostic information for clinicians. Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own algorithm for Alzheimer's Disease diagnostics based on a convolutional neural network and sMRI and DTI modalities fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (this http URL). Comparison with a single modality approach shows promising results. We also propose our own method of data augmentation for balancing classes of different size and analyze the impact of the ROI size on the classification results as well. BACKGROUND: Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI). METHODS: We applied a Support Vector Machine (SVM) classifier to DTI and volumetric magnetic resonance imaging data from 35 amyloid-β42 negative MCI subjects (MCI-Aβ42-), 35 positive MCI subjects (MCI-Aβ42+), and 25 healthy controls (HC) retrieved from the European DTI Study on Dementia. The SVM was applied to DTI-derived fractional anisotropy, mean diffusivity (MD), and mode of anisotropy (MO) maps. For comparison, we studied classification based on gray matter (GM) and WM volume. RESULTS: We obtained accuracies of up to 68 for MO and 63 for GM volume when it came to distinguishing between MCI-Aβ42- and MCI-Aβ42+. When it came to separating MCI-Aβ42+ from HC we achieved an accuracy of up to 77 for MD and a significantly lower accuracy of 68 for GM volume. The accuracy of multimodal classification was not higher than the accuracy of the best single modality. CONCLUSIONS: Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.