Topological data analysis of single-trial electroencephalographic signals
Type
ArticleKAUST Department
Biostatistics GroupComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program
Date
2018-09-11Online Publication Date
2018-09-11Print Publication Date
2018-09Permanent link to this record
http://hdl.handle.net/10754/631519
Metadata
Show full item recordAbstract
Epilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. Statistical analysis of neuro-physiological recordings, such as electroencephalography (EEG), facilitates the understanding of epileptic seizures. Standard statistical methods typically analyze amplitude and frequency information in EEG signals. In the current study, we propose a topological data analysis (TDA) framework to analyze single-trial EEG signals. The framework denoises signals with a weighted Fourier series (WFS), and tests for differences between the topological features—persistence landscapes (PLs) of denoised signals through resampling in the frequency domain. Simulation studies show that the test is robust for topologically similar signals while bearing sensitivity to topological tearing in signals. In an application to single-trial epileptic EEG signals, EEG signals in the diagnosed seizure origin and its symmetric site are found to have similar PLs before and during a seizure attack, in contrast to signals at other sites showing significant statistical difference in the PLs of the two phases.Citation
Wang Y, Ombao H, Chung MK (2018) Topological data analysis of single-trial electroencephalographic signals. The Annals of Applied Statistics 12: 1506–1534. Available: http://dx.doi.org/10.1214/17-AOAS1119.Sponsors
Supported in part by NIH Brain Initiative Grant EB022856 Supported in part by NSF DMS, NSF SES and the KAUST Baseline Research Fund.Publisher
Institute of Mathematical StatisticsJournal
The Annals of Applied StatisticsAdditional Links
https://projecteuclid.org/euclid.aoas/1536652963ae974a485f413a2113503eed53cd6c53
10.1214/17-AOAS1119