Vergence Variability: A Key to Understanding Oculomotor Adaptability?
Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic (the presbyopic groups aged above 45 years) to determine how the variability of the eye movements made by the populations differs. The variability...
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| Published in: | 2006 International Conference of the IEEE Engineering in Medicine and Biology Society Vol. Supplement; pp. 6777 - 6780 |
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| Main Authors: | , , |
| Format: | Conference Proceeding Journal Article |
| Language: | English |
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United States
IEEE
2006
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| ISBN: | 9781424400324, 1424400325 |
| ISSN: | 1557-170X |
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| Abstract | Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic (the presbyopic groups aged above 45 years) to determine how the variability of the eye movements made by the populations differs. The variability was determined using Shannon Entropy calculations of Wavelet transform coefficients, to yield a non-linear analysis of the vergence movement variability. The data were then fed through a kmeans clustering algorithm to classify each subject, with no a priori knowledge of true subject classification. The results indicate a highly significant difference in the total entropy values between the three groups, indicating a difference in the level of information content, and thus hypothetically the oculomotor adaptability, between the three groups. Further, the frequency distribution of the entropy varied across groups. |
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| AbstractList | Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic (the presbyopic groups aged above 45 years) to determine how the variability of the eye movements made by the populations differs. The variability was determined using Shannon Entropy calculations of Wavelet transform coefficients, to yield a non-linear analysis of the vergence movement variability. The data were then fed through a kmeans clustering algorithm to classify each subject, with no a priori knowledge of true subject classification. The results indicate a highly significant difference in the total entropy values between the three groups, indicating a difference in the level of information content, and thus hypothetically the oculomotor adaptability, between the three groups. Further, the frequency distribution of the entropy varied across groups. Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic(the presbyopic groups aged above 45 years) to determine how the variability of the eye movements made by the populations differs. The variability was determined using Shannon Entropy calculations of Wavelet transform coefficients, to yield a non-linear analysis of the vergence movement variability. The data were then fed through a k-means clustering algorithm to classify each subject, with no a priori knowledge of true subject classification. The results indicate a highly significant difference in the total entropy values between the three groups, indicating a difference in the level of information content, and thus hypothetically the oculomotor adaptability, between the three groups.Further, the frequency distribution of the entropy varied across groups. Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic(the presbyopic groups aged above 45 years) to determine how the variability of the eye movements made by the populations differs. The variability was determined using Shannon Entropy calculations of Wavelet transform coefficients, to yield a non-linear analysis of the vergence movement variability. The data were then fed through a k-means clustering algorithm to classify each subject, with no a priori knowledge of true subject classification. The results indicate a highly significant difference in the total entropy values between the three groups, indicating a difference in the level of information content, and thus hypothetically the oculomotor adaptability, between the three groups.Further, the frequency distribution of the entropy varied across groups.Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic(the presbyopic groups aged above 45 years) to determine how the variability of the eye movements made by the populations differs. The variability was determined using Shannon Entropy calculations of Wavelet transform coefficients, to yield a non-linear analysis of the vergence movement variability. The data were then fed through a k-means clustering algorithm to classify each subject, with no a priori knowledge of true subject classification. The results indicate a highly significant difference in the total entropy values between the three groups, indicating a difference in the level of information content, and thus hypothetically the oculomotor adaptability, between the three groups.Further, the frequency distribution of the entropy varied across groups. |
| Author | Petrock, Anne Marie Alvarez, T. Reisman, S. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17959510$$D View this record in MEDLINE/PubMed |
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| Snippet | Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic (the... Vergence eye movements were recorded from three different populations: healthy young (ages 18-35 years), adaptive presbyopic and non-adaptive presbyopic(the... |
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| SubjectTerms | Adolescent Adult Aging Aging - physiology Algorithms Cities and towns Displays Entropy Eye Movements - physiology Frequency Heart rate variability Humans Lenses Middle Aged USA Councils Vision defects Wavelet analysis |
| Title | Vergence Variability: A Key to Understanding Oculomotor Adaptability? |
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