Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea

被引：0
|
作者：
Manjunath, Shashank [1 ]
Perea, Jose A. [1 ,2 ]
Sathyanarayana, Aarti [1 ,3 ,4 ]
机构：
[1] Northeastern Univ, Khoury Coll Comp Sci, Boston, MA 02115 USA
[2] Northeastern Univ, Dept Math, Boston, MA 02115 USA
[3] Northeastern Univ, Bouve Coll Hlth Sci, Boston, MA 02115 USA
[4] Harvard Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
来源：
2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC | 2023年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/EMBC40787.2023.10340674
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Topological data analysis (TDA) is an emerging technique for biological signal processing. TDA leverages the invariant topological features of signals in a metric space for robust analysis of signals even in the presence of noise. In this paper, we leverage TDA on brain connectivity networks derived from electroencephalogram (EEG) signals to identify statistical differences between pediatric patients with obstructive sleep apnea (OSA) and pediatric patients without OSA. We leverage a large corpus of data, and show that TDA enables us to see a statistical difference between the brain dynamics of the two groups.
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页数：4
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