Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium

Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium

Abstract Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analy...

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Autores principales: Takehiko Yamanashi, Mari Kajitani, Masaaki Iwata, Kaitlyn J. Crutchley, Pedro Marra, Johnny R. Malicoat, Jessica C. Williams, Lydia R. Leyden, Hailey Long, Duachee Lo, Cassidy J. Schacher, Kazuaki Hiraoka, Tomoyuki Tsunoda, Ken Kobayashi, Yoshiaki Ikai, Koichi Kaneko, Yuhei Umeda, Yoshimasa Kadooka, Gen Shinozaki
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Publicado: Nature Portfolio 2021
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spelling oai:doaj.org-article:54b1369dec3540ab92d5f0aae72337782021-12-02T15:22:58ZTopological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium10.1038/s41598-020-79391-y2045-2322https://doaj.org/article/54b1369dec3540ab92d5f0aae72337782021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79391-yhttps://doaj.org/toc/2045-2322Abstract Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.Takehiko YamanashiMari KajitaniMasaaki IwataKaitlyn J. CrutchleyPedro MarraJohnny R. MalicoatJessica C. WilliamsLydia R. LeydenHailey LongDuachee LoCassidy J. SchacherKazuaki HiraokaTomoyuki TsunodaKen KobayashiYoshiaki IkaiKoichi KanekoYuhei UmedaYoshimasa KadookaGen ShinozakiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Takehiko Yamanashi
Mari Kajitani
Masaaki Iwata
Kaitlyn J. Crutchley
Pedro Marra
Johnny R. Malicoat
Jessica C. Williams
Lydia R. Leyden
Hailey Long
Duachee Lo
Cassidy J. Schacher
Kazuaki Hiraoka
Tomoyuki Tsunoda
Ken Kobayashi
Yoshiaki Ikai
Koichi Kaneko
Yuhei Umeda
Yoshimasa Kadooka
Gen Shinozaki
Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium
description Abstract Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.
format article
author Takehiko Yamanashi
Mari Kajitani
Masaaki Iwata
Kaitlyn J. Crutchley
Pedro Marra
Johnny R. Malicoat
Jessica C. Williams
Lydia R. Leyden
Hailey Long
Duachee Lo
Cassidy J. Schacher
Kazuaki Hiraoka
Tomoyuki Tsunoda
Ken Kobayashi
Yoshiaki Ikai
Koichi Kaneko
Yuhei Umeda
Yoshimasa Kadooka
Gen Shinozaki
author_facet Takehiko Yamanashi
Mari Kajitani
Masaaki Iwata
Kaitlyn J. Crutchley
Pedro Marra
Johnny R. Malicoat
Jessica C. Williams
Lydia R. Leyden
Hailey Long
Duachee Lo
Cassidy J. Schacher
Kazuaki Hiraoka
Tomoyuki Tsunoda
Ken Kobayashi
Yoshiaki Ikai
Koichi Kaneko
Yuhei Umeda
Yoshimasa Kadooka
Gen Shinozaki
author_sort Takehiko Yamanashi
title Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium
title_short Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium
title_full Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium
title_fullStr Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium
title_full_unstemmed Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium
title_sort topological data analysis (tda) enhances bispectral eeg (bseeg) algorithm for detection of delirium
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/54b1369dec3540ab92d5f0aae7233778
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