Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM

Unlike age related dementia, Alzheimer’s disease is more progressive and causes rapid deterioration in patient’s cognitive functions. Prior to its first clinical manifestation, it is evident that the brain damaging process has already been commenced much earlier in life. This asymptomatic period could have spanned as long as a decade or more. Although there is not yet ultimate cure for the disease, the sooner it is diagnosed, the more chance that available therapeutic measures could improve patient’s quality of life. Standard medical questionnaire and medical imaging are the most prevailing means of identifying early Alzheimer’s disease. Despite a great effort having been made in analyzing structural atrophy in human brain by using CT and MRI, the recent attempts have reached high accuracy and precision but relatively poor sensitivity. Functional imaging such as PET is of much lower spatial resolution but promising modality taken to elevate this limitation. This paper presents a classification method for early detection of the disease from PET scans drawn from Thai population. However, instead of conventional structural analysis, this study performed clustering on unwrapped signals, transformed from imaging data by using Mel-Frequency Cepstral Coefficients (MFCC), by a generic Support Vector Machine (SVM) classifier. The experimental results reported herein indicates that, with optimal MFCC order, the proposed method could identify subjects with Alzheimer’s from controls, with high accuracy, precision and specificity. With a cross-validation ratio of 8:2 and a linear SVM kernel, the classification accuracy, sensitivity and specificity were 96.51, 93.98, and 97.77, respectively, and increased as the MFCC orders

Development of Anatomical and Functional Magnetic Resonance Imaging Measures of Alzheimer Disease

Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. Since the hippocampus is considered to be one of the first brain structures affected by Alzheimer disease, in our first study a reliable and fully automated approach was developed to quantify medial temporal lobe atrophy using magnetic resonance imaging. This measurement of medial temporal lobe atrophy showed differences (pnovel biomarker of brain activity was developed based on a first-order textural feature of the resting state functional magnetic resonance imagining signal. The mean brain activity metric was shown to be significantly lower (pp18F labeled fluorodeoxyglucose positron emission tomography. In the final study, we examine whether combined measures of gait and cognition could predict medial temporal lobe atrophy over 18 months in a small cohort of people (N=22) with mild cognitive impairment. The results showed that measures of gait impairment can help to predict medial temporal lobe atrophy in people with mild cognitive impairment. The work in this thesis contributes to the growing evidence the specific magnetic resonance imaging measures of brain structure and function can be used to identify and monitor the progression of Alzheimer’s disease. Continued refinement of these methods, and larger longitudinal studies will be needed to establish whether the specific metrics of brain dysfunction developed in this thesis can be of clinical benefit and aid in drug development