scholarly journals Diffusion structural network metrics as a predictor for Alzheimer’s disease diagnosis in Down syndrome using support vector machine classification

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Stephanie SG Brown ◽  
Elijah Mak ◽  
Monika Grigorova ◽  
Jessica Beresford‐Webb ◽  
Madeleine Jane Walpert ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Down Syndrome ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Support Vector Machine Classification ◽  
Structural Network ◽  
Alzheimer’S Disease Diagnosis ◽  
Network Metrics

scholarly journals Support vector machine based alzheimer’s disease diagnosis using synchrony features

2020 ◽  
Vol 9 (1) ◽  
pp. 57
Author(s):  
Nilesh Kulkarni
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Early Stage ◽  
Research Work ◽  
Normal Subjects ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Experimental Database ◽  
Wavelet Features

Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.

scholarly journals Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 941
Author(s):  
Hamid Akramifard ◽  
MohammadAli Balafar ◽  
SeyedNaser Razavi ◽  
Abd Rahman Ramli
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Subset ◽  
Alzheimer’S Disease Diagnosis

In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer’s disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model’s accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer’s disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

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