Method of early detection of multiple sclerosis
First Claim
1. A method of early detection of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
- (a) placing one or more study animals in one cage in a vivarium;
(b) electronically observing one or animal activities in real-time of the one or more study animals using a combination of electronic cameras, hardware, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware;
(c) selecting a single “
activity-drop metric”
with an associated scalar “
activity-drop value,”
responsive to the electronically observed one or more animal activities in step (b);
(d) selecting an “
MS health detection function”
whose input comprises an “
animal health dataset” and
whose output comprises a likelihood scalar representing the likelihood that at least one of the study animals has MS;
(e) collecting a nightly activity scalar of the at least one of the study animals repeatedly and continually for a night and placing the nightly activity scalar into a set of “
nightly activity data”
;
(f) identifying automatically three consecutive time regions in the nightly activity data;
a “
high-activity region,”
an “
activity-drop region,” and
a “
low-activity region”
;
(g) applying the activity-drop metric to the three consecutive regions, generating a nightly activity-drop value for the at least one of the study animals;
(h) adding the nightly activity-drop value into the animal health dataset, wherein the animal health dataset comprises the resulting nightly activity-drop values;
(i) applying the MS health detection function to the animal health dataset, generating a likelihood scalar for each iteration;
(j) iterating steps (e) through (i) for sequential nights until a terminating condition is reached;
wherein the early detection of MS comprises the likelihood scalars from step (i), of the at least one of the study animals;
(k) terminating the study when the terminating condition is reached.
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Accused Products
Abstract
A method of early detection of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal'"'"'s activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, a health detection function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or detects MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.
6 Citations
9 Claims
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1. A method of early detection of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
-
(a) placing one or more study animals in one cage in a vivarium; (b) electronically observing one or animal activities in real-time of the one or more study animals using a combination of electronic cameras, hardware, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware; (c) selecting a single “
activity-drop metric”
with an associated scalar “
activity-drop value,”
responsive to the electronically observed one or more animal activities in step (b);(d) selecting an “
MS health detection function”
whose input comprises an “
animal health dataset” and
whose output comprises a likelihood scalar representing the likelihood that at least one of the study animals has MS;(e) collecting a nightly activity scalar of the at least one of the study animals repeatedly and continually for a night and placing the nightly activity scalar into a set of “
nightly activity data”
;(f) identifying automatically three consecutive time regions in the nightly activity data;
a “
high-activity region,”
an “
activity-drop region,” and
a “
low-activity region”
;(g) applying the activity-drop metric to the three consecutive regions, generating a nightly activity-drop value for the at least one of the study animals; (h) adding the nightly activity-drop value into the animal health dataset, wherein the animal health dataset comprises the resulting nightly activity-drop values; (i) applying the MS health detection function to the animal health dataset, generating a likelihood scalar for each iteration; (j) iterating steps (e) through (i) for sequential nights until a terminating condition is reached; wherein the early detection of MS comprises the likelihood scalars from step (i), of the at least one of the study animals; (k) terminating the study when the terminating condition is reached. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification