Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network.

Automatic speech emotion recognition is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. We propose a novel deep neural architecture to extract the informative feature representations from the heterogeneous acoustic feature groups which may contain redundant and unrelated information leading to low emotion recognition performance in this work. After obtaining the informative features, a fusion network is trained to jointly learn the discriminative acoustic feature representation and a Support Vector Machine (SVM) is used as the final classifier for recognition task. Experimental results on the IEMOCAP dataset demonstrate that the proposed architecture improved the recognition performance, achieving accuracy of 64% compared to existing state-of-the-art approaches.

Predicting the development of mild cognitive impairment: a new use of pattern recognition.

While the conversion from mild cognitive impairment to Alzheimer's disease has received much recent attention, the transition from normal cognition to mild cognitive impairment is largely unexplored. The present pattern recognition study addressed this by using neuropsychological test scores and neuroimaging morphological measures to predict the later development of mild cognitive impairment in cognitively normal community-dwelling individuals aged 70-90years. A feature selection algorithm chose a subset of neuropsychological and FreeSurfer-derived morphometric features that optimally differentiated between individuals who developed mild cognitive impairment and individuals who remained cognitively normal. Support vector machines were used to train classifiers and test prediction performance, which was evaluated via 10-fold cross-validation to reduce variability. Prediction performance was greater when using a combination of neuropsychological scores and morphological measures than when using either of these alone. Results for the combined method were: accuracy 78.51%, sensitivity 73.33%, specificity 79.75%, and an area under the receiver operating characteristic curve of 0.841. Of all the features investigated, memory performance and measures of the prefrontal cortex and parietal lobe were the most discriminative. Our prediction method offers the potential to detect elderly individuals with apparently normal cognition at risk of imminent cognitive decline. Identification at this stage will facilitate the early start of interventions designed to prevent or slow the development of Alzheimer's disease and other dementias.

Cortical gyrification and sulcal spans in early stage Alzheimer's disease.

Alzheimer's disease (AD) is characterized by an insidious onset of progressive cerebral atrophy and cognitive decline. Previous research suggests that cortical folding and sulcal width are associated with cognitive function in elderly individuals, and the aim of the present study was to investigate these morphological measures in patients with AD. The sample contained 161 participants, comprising 80 normal controls, 57 patients with very mild AD, and 24 patients with mild AD. From 3D T1-weighted brain scans, automated methods were used to calculate an index of global cortex gyrification and the width of five individual sulci: superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure. We found that global cortex gyrification decreased with increasing severity of AD, and that the width of all individual sulci investigated other than the intra-parietal sulcus was greater in patients with mild AD than in controls. We also found that cognitive functioning, as assessed by Mini-Mental State Examination (MMSE) scores, decreased as global cortex gyrification decreased. MMSE scores also decreased in association with a widening of all individual sulci investigated other than the intra-parietal sulcus. The results suggest that abnormalities of global cortex gyrification and regional sulcal span are characteristic of patients with even very mild AD, and could thus facilitate the early diagnosis of this condition.

Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach.

Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a sample of community-dwelling adults aged 70-90 years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS+LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09%; sensitivity, 51.96%; specificity, 78.40%; and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of individuals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults.

Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors.

Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

The relationship between cortical sulcal variability and cognitive performance in the elderly.

The relationship between cognitive functions and brain structure has been of long-standing research interest. Most previous research has attempted to relate cognition to volumes of specific brain structures or thickness of cortical regions, with relatively few studies examining other features such as cortical surface anatomy. In this study, we examine the relationship between cortical sulcal features and cognitive function in a sample (N=316) of community-dwelling subjects aged between 70 and 90 years (mean=78.06±4.75; male/female=130/186) who had detailed neuropsychological assessments and brain MRI scans. Using automated methods on 3D T1-weighted brain scans, we computed global sulcal indices (g-SIs) of the whole brain and average sulcal spans of five prominent sulci. The g-SI, which reflects the complexity of sulcal folds across the cerebral hemispheres, showed a significant positive correlation with performance in most cognitive domains including attention/processing speed, memory, language and executive function. Regionally, a negative correlation was found between some cognitive functions and sulcal spans, i.e. poorer cognitive performance was associated with a wider sulcal span. Of the five cognitive domains examined, the performance of processing speed was found to be correlated with the spans of most sulci, with the strongest correlation being with the superior temporal sulcus. Memory did not show a significant correlation with any individual sulcal index, after correcting for age and sex. Of the five sulci measured, the left superior temporal sulcus showed the highest sensitivity, with significant correlations with performances in all cognitive domains except memory, after controlling for age, sex, years of education and brain size. The results suggest that regionally specific sulcal morphology is associated with cognitive function in elderly individuals.

The effects of age and sex on cortical sulci in the elderly.

A large number of structural brain studies using magnetic resonance imaging (MRI) have reported age-related cortical changes and sex difference in brain morphology. Most studies have focused on cortical thickness or density, with relatively few studies of cortical sulcal features, especially in the elderly. In this paper, we report global sulcal indices (g-SIs) of both cerebral hemispheres and the average sulcal span in six prominent sulci, as observed in T1-weighted scans obtained from a large community cohort of 319 non-demented individuals aged between 70 and 90 years (mean=78.06+/-4.75; male/female=149/170), using automated methods. Our results showed that for both hemispheres, g-SIs had significant negative correlations with age in both men and women. Using an interactive effect analysis, we found that g-SIs for men declined faster with age than that for women. The widths of all six sulcal spans increased significantly with age, with largest span increase occurring in the superior frontal sulcus. Compared to women, men had significantly wider sulcal spans for all sulci that were examined. Our findings suggest that both age and sex contribute to significant cortical gyrification differences and variations in the elderly. This study establishes a reference for future studies of age-related brain changes and neurodegenerative diseases in the elderly.

Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory.

Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.