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Volume 09
February 15-16, 2019 Amsterdam| Netherlands
Vascular Dementia
11 th International Conference on
Vascular Dementia 2019 Journal of Alzheimers Disease & Parkinsonism | ISSN : 2161-0460
Classification of demented and non-demented patients using
longitudinal statistics
In this work, we propose a framework to do classification between demented and non-
demented patients using longitudinal statistical analysis. Though in practice, we often
use the detailed clinical findings to establish a possible hypothesis about, whether the
patient has dementia or not, nonetheless an automatic algorithm to identify a patient to be
demented (D)/ non-demented (ND) is required. Some of the recent works in this regime
includes [1,2,3]. We propose an algorithm for this automatic identification based on a
sequence of MR scans collected over a time period. The proposed algorithm is based
on the research of the second author have done during his PhD at University of Florida,
USA. Before going into the detail of our experimental setup, we will briefly describe the
proposed algorithm. Given a temporal sequence of MR scans for a patient (identified
with a path on a high dimensional hypersphere), we identified the temporal sequence as
the “average” of the data points on the path and the variation, captured by the principal
subspace. This identification is robust to any affine transformation of the path including
rotation, translation of the MR scans. Notice that in the collection time of MR scans, the
scans can be a transformation of each other due to several aspects including alignment
of a patient, MRI machine etc. In order to test our proposed framework, we used the
benchmark OASIS dataset, which consists of at least two MR brain scans of each of the
150 subjects, aged between 60 to 90 years. In order to avoid gender effects, we have used
MR scans of male patients from three visits separated by at least one year. In our dataset,
we have 12 ND and 11 D subjects. We have computed an atlas from the MR scans and
non-rigidly register each scan to the atlas to identify each MR scan as a point on the unit
hyperpshere of dimension 892. For each subject, this essentially gives us a path on the
hypersphere. After using our representation mentioned in the previous paragraph, for each
subject we have identification with a point on the hypersphere and a subspace (capturing
the variation in the path). We used a standard nearest average classifier. We first computed
the average path for each class (D and ND) over the training data. Then for a test subject
we assign it to the class with the nearest average path. Due to the small amount of data,
we have chosen a leave-one-out framework, i.e., we randomly put aside one subject from
each class and then use the rest of the subjects for training and repeat this process. Using
this classification framework, we can correctly identify 11 out of 12 ND subjects and 10
out of 11 D subjects, achieving 91.3% classification accuracy. The above experimentation
is a clear indication of the usefulness of an automatic differentiation technique between
demented and non-demented subjects. As a future direction, we want to investigate our
proposed framework on large scale data t sets..
Biography
Dr. Shyamal Chakraborty did his graduation and post-graduation from Calcutta University. He is attached to
many NGO-s like Sevac,Asha Bhavan,Paripurnata and also appointed as part-time Psychiatrist in Correctional
Home in Calcutta. He is a life fellow of Indian Psychiatric Society,Founder fellow of Indian Association of Private
Psychiatry, Member of Indian Medical Association, Member of American Association of Geriatric Psychiatry. He is
doing practice in Neuropsychiatry since 1990 and attached to Apollo Clinic attended many conferences
under APA,WPA,EPA. He presented poster in ADHD conference in Italy. He chaired many conferences under Indian
Psychiatric is a National Scholar and his abstracts are published in many journals in India as well as
abroad.
Shyamal Chakraborty
Kothari Medical Centre, India
Shyamal Chakraborty, J Alzheimers Dis Parkinsonism 2019, Volume 09
DOI: 10.4172/2161-0460-C1-060