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Liu Z, Lei Y, Li X, Zhang X, Yang H, Yu J, Gu Y, Ma Y. Analysis on Intraoperative Electrocorticogram Characteristics for Evaluating the Risk of Postoperative Epilepsy . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083022 DOI: 10.1109/embc40787.2023.10341027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is one of the most common complications after craniotomy, which happens suddenly and does great harm. There still lacks of effective prediction method during the operation. The main purpose of this paper is to explore the correlation between the characteristics of intraoperative electrocorticogram (ECoG) and postoperative epilepsy, and select effective features to establish a prediction model. This retrospective study uses intraoperative ECoG recordings of 144 patients with cerebrovascular diseases undergoing cerebral revascularization surgeries. The cases are divided into subtypes of ischemic and hemorrhagic. Nine types of ECoG features are designed on different frequency bands indicating clinical information, power spectrum, complexity, sequence change, and information quantity, while their changes in different surgical stages are also considered. Then statistical analysis is used to obtain features significantly related to postoperative epilepsy (p<0.05). The sparse representation method is used on these features to further screen and reduce the redundancy, and then machine learning methods are used to establish a prediction model for postoperative epilepsy. The accuracy, sensitivity and specificity of the best prediction model can achieve 0.817, 0.800 and 0.833 respectively under 5-fold cross validation.Clinical Relevance-This study explores the correlation between the characteristics of intraoperative ECoG and postoperative epilepsy, investigates the possibility to use the ECoG features and machine learning algorithms to assess the risk of postoperative epilepsy during the surgery. Further results are expected to provide reference for preventive measures to reduce the occurrence of postoperative epilepsy.
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Yamanashi T, Crutchley KJ, Wahba NE, Sullivan EJ, Comp KR, Kajitani M, Tran T, Modukuri MV, Marra PS, Herrmann FM, Chang G, Anderson ZEM, Iwata M, Kobayashi K, Kaneko K, Umeda Y, Kadooka Y, Lee S, Shinozaki E, Karam MD, Noiseux NO, Shinozaki G. Evaluation of point-of-care thumb-size bispectral electroencephalography device to quantify delirium severity and predict mortality. Br J Psychiatry 2021; 220:1-8. [PMID: 35049468 DOI: 10.1192/bjp.2021.101] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes. AIMS To improve the BSEEG method by introducing a new EEG device. METHOD In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed. RESULTS We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality. CONCLUSIONS We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.
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Affiliation(s)
- Takehiko Yamanashi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, California, USA; and Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA; and Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Japan
| | - Kaitlyn J Crutchley
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA; and School of Medicine, University of Nebraska Medical Center, Nebraska, USA
| | - Nadia E Wahba
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Eleanor J Sullivan
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Katie R Comp
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | | | - Tammy Tran
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Manisha V Modukuri
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Pedro S Marra
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Felipe M Herrmann
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Gloria Chang
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Zoe-Ella M Anderson
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa,USA
| | - Masaaki Iwata
- Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Japan
| | | | - Koichi Kaneko
- Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Japan
| | | | | | - Sangil Lee
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Eri Shinozaki
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Matthew D Karam
- Department of Orthopedic Surgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Nicolas O Noiseux
- Department of Orthopedic Surgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Gen Shinozaki
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, California, USA; and Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
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Yamanashi T, Kajitani M, Iwata M, Crutchley KJ, Marra P, Malicoat JR, Williams JC, Leyden LR, Long H, Lo D, Schacher CJ, Hiraoka K, Tsunoda T, Kobayashi K, Ikai Y, Kaneko K, Umeda Y, Kadooka Y, Shinozaki G. Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium. Sci Rep 2021; 11:304. [PMID: 33431928 PMCID: PMC7801387 DOI: 10.1038/s41598-020-79391-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/08/2020] [Indexed: 12/11/2022] Open
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.
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Affiliation(s)
- Takehiko Yamanashi
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA.,Department of Neuropsychiatry, Faculty of Medicine, Tottori University, Yonago, Japan
| | | | - Masaaki Iwata
- Department of Neuropsychiatry, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Kaitlyn J Crutchley
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Pedro Marra
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Johnny R Malicoat
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Jessica C Williams
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Lydia R Leyden
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Hailey Long
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Duachee Lo
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Cassidy J Schacher
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | | | | | | | | | - Koichi Kaneko
- Department of Neuropsychiatry, Faculty of Medicine, Tottori University, Yonago, Japan
| | | | | | - Gen Shinozaki
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA. .,Department of Neurosurgery, University of Iowa Carver College of Medicine, Iowa City, IA, USA. .,Department of Anesthesia, University of Iowa Carver College of Medicine, Iowa City, IA, USA. .,Iowa Neuroscience Institute, Iowa City, IA, USA. .,Interdisciplinary Graduate Program in Neuroscience, University of Iowa, 25 S Grand Ave. Medical Laboratories B002, Iowa City, IA, 52246, USA.
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Bispectral EEG (BSEEG) quantifying neuro-inflammation in mice induced by systemic inflammation: A potential mouse model of delirium. J Psychiatr Res 2021; 133:205-211. [PMID: 33360427 DOI: 10.1016/j.jpsychires.2020.12.036] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/03/2020] [Accepted: 12/09/2020] [Indexed: 02/06/2023]
Abstract
Most of the animal studies using inflammation-induced cognitive change have relied on behavioral testing without objective and biologically solid methods to quantify the severity of cognitive disturbances. We have developed a bispectral EEG (BSEEG) method using a novel algorithm in clinical study. This method effectively differentiates between patients with and without delirium, and predict long-term mortality. In the present study, we aimed to apply our bispectral EEG (BSEEG) method, which can detect patients with delirium, to a mouse model of delirium with systemic inflammation induced by lipopolysaccharides (LPS) injection. We recorded EEG after LPS injection using wildtype early adulthood mice (2~3-month-old) and aged mice (18-19-month-old). Animal EEG recordings were converted for power spectral density to calculate BSEEG score using the similar BSEEG algorithm previously developed for our human study. The BSEEG score was relatively stable and slightly high during the day. Alternatively, the BSEEG score was erratic and low in average during the night. LPS injection increased the BSEEG score dose-dependently and diminished the diurnal changes. The mean BSEEG score increased much more in the aged mice group as dosage increased. Our results suggest that BSEEG method can objectively "quantify" level of neuro-Inflammation induced by systemic inflammation (LPS), and that this BSEEG method can be useful as a model of delirium in mice.
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Yamanashi T, Iwata M, Crutchley KJ, Sullivan EJ, Malicoat JR, Anderson ZEM, Marra PS, Chang G, Kaneko K, Shinozaki E, Lee S, Shinozaki G. New Cutoff Scores for Delirium Screening Tools to Predict Patient Mortality. J Am Geriatr Soc 2020; 69:140-147. [PMID: 32905636 DOI: 10.1111/jgs.16815] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/11/2020] [Accepted: 08/11/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND/OBJECTIVES Detecting delirium is important to identify patients with a high risk of poor outcomes. Although many different kinds of screening instruments for delirium exist, there is no solid consensus about which methods are the most effective. In addition, it is important to find the most useful tools in predicting outcomes such as mortality. DESIGN Retrospective cohort study. SETTING University of Iowa Hospitals and Clinics. PARTICIPANTS A total of 1,125 adult inpatients (mean age = 67.7; median age = 69). MEASUREMENTS Post hoc analyses were performed based on existing data from the Confusion Assessment Method for Intensive Care Unit (CAM-ICU), Delirium Rating Scale-Revised-98 (DRS), and the Delirium Observation Screening Scale (DOSS). Correlation among these scales and relationships between 365-day mortality and each scale were evaluated. RESULTS A positive result on the CAM-ICU ("CAM-ICU positive") was associated with higher DRS and DOSS scores. A DRS score = 9/10 was the best cutoff to detect CAM-ICU positive, and DOSS = 2/3 was the best cutoff to detect CAM-ICU positive. CAM-ICU positive was associated with high 365-day mortality. DRS score = 9/10 and DOSS score = 0/1 were found to differentiate mortality risk the most significantly. Higher DRS and DOSS scores significantly coincided with a decrease in a patient's survival rate at 365 days. CONCLUSION The best DRS and DOSS cutoff scores to differentiate 365-day mortality risk were lower than those commonly used to detect delirium in the literature. New cutoff scores for the DRS and DOSS might be useful in differentiating risk of mortality among hospital patients.
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Affiliation(s)
- Takehiko Yamanashi
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.,Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Tottori, Japan
| | - Masaaki Iwata
- Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Tottori, Japan
| | - Kaitlyn J Crutchley
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Eleanor J Sullivan
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Johnny R Malicoat
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Zoe-Ella M Anderson
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Pedro S Marra
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Gloria Chang
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Koichi Kaneko
- Department of Neuropsychiatry, Tottori University Faculty of Medicine, Yonago, Tottori, Japan
| | - Eri Shinozaki
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Sangil Lee
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Gen Shinozaki
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.,Department of Neurosurgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.,Department of Anesthesia, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.,Iowa Neuroscience Institute, Iowa City, Iowa, USA.,Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, Iowa, USA
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