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 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.

Mortality among patients with sepsis associated with a bispectral electroencephalography (BSEEG) score

“…Our previous studies showed that the BSEEG approach can detect delirium subjects 13 , 15 , 16 and predict poor outcomes 14 . It has also been shown that increased BSEEG score was observed in lipopolysaccharides-induced delirium mouse model 23 .…”

“…We collected raw EEG data and calculated BSEEG score as previously described 13 – 15 . Briefly, EEG was collected using a portable, handheld EEG device (CMS2100, CONTEC, Qinhuangdao, Hebei, China) up to twice daily during the daytime.…”

“…EEG was recorded for 3–5 min and up to 10 min. The obtained raw EEG data was converted into spectral density plots, and the signal-processing algorithm was used to produce a BSEEG score based on a ratio between low-frequency (3 Hz) power to high-frequency (10 Hz) power 13 – 15 . Then, the normalized BSEEG score was determined by the number of standard deviations (SDs) from the mean of our accumulated data 14 .…”

“…It is reported that more than 30% of patients with sepsis have SAE 9 , and delirium/encephalopathy and mild EEG slowing seem to be one of its earliest features 10 , 11 . As we have previously developed a point-of-care BSEEG method, which was shown to be effective in detecting delirium and predicting patient outcomes 13 – 15 , in this study, we aimed to apply the BSEEG approach to see if the same EEG-based method can be useful for the identification of patients with sepsis at high risk for poor outcomes.…”

Mortality prediction by bispectral electroencephalography among 502 patients: its role in dementia

“… 6 , 7 We previously developed a method to detect delirium by using the bispectral electroencephalography (BSEEG) score. 7 , 8 Higher BSEEG scores were associated with higher mortality independent of delirium status. 9 In fact, we showed that BSEEG score can predict mortality of elderly inpatients better than clinical categorization of delirium.…”