Cite Article

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

Choose citation format

BibTeX

@article{IJASEIT6503,
   author = {Jantana Panyavaraporn and Paramate Horkaew},
   title = {Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {5},
   year = {2018},
   pages = {1829--1835},
   keywords = {PET images; Alzheimer’s Disease; MFCC; SVM},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6503},
   doi = {10.18517/ijaseit.8.5.6503}
}

EndNote

%A Panyavaraporn, Jantana
%A Horkaew, Paramate
%D 2018
%T Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM
%B 2018
%9 PET images; Alzheimer’s Disease; MFCC; SVM
%! Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM
%K PET images; Alzheimer’s Disease; MFCC; SVM
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6503
%R doi:10.18517/ijaseit.8.5.6503
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 5
%@ 2088-5334

IEEE

Jantana Panyavaraporn and Paramate Horkaew,"Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, pp. 1829-1835, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.5.6503.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Panyavaraporn, Jantana
AU  - Horkaew, Paramate
PY  - 2018
TI  - Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 5
Y2  - 2018
SP  - 1829
EP  - 1835
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - PET images; Alzheimer’s Disease; MFCC; SVM
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6503
DO  - 10.18517/ijaseit.8.5.6503

RefWorks

RT Journal Article
ID 6503
A1 Panyavaraporn, Jantana
A1 Horkaew, Paramate
T1 Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 5
YR 2018
SP 1829
OP 1835
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 PET images; Alzheimer’s Disease; MFCC; SVM
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6503
DO  - 10.18517/ijaseit.8.5.6503