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Vrijdag XCE, Hallum LE, Tonks EI, van Waart H, Mitchell SJ, Sleigh JW. Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra. J Clin Monit Comput 2024; 38:363-371. [PMID: 37440117 PMCID: PMC10995006 DOI: 10.1007/s10877-023-01054-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/25/2023] [Indexed: 07/14/2023]
Abstract
Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. Features of the delta-, theta-, alpha- and beta-band power were used within a 12-fold, participant-wise cross-validation framework to train and test two SVMs: (1) binary SVM classifying EEG during 0 or 40% exposure (chance = 50%); (2) multi-class SVM classifying EEG during 0, 20, 30 or 40% exposure (chance = 25%). Both the binary (accuracy 92%) and the multi-class (accuracy 52%) SVMs classified EEG recordings at rates significantly better than chance (p < 0.001 and p = 0.01, respectively). To determine the relative importance of frequency band features for classification accuracy, we systematically removed features before re-training and re-testing the SVMs. This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.
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Affiliation(s)
- Xavier C E Vrijdag
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
| | - Luke E Hallum
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland, 1142, New Zealand
| | - Emma I Tonks
- Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland, 1142, New Zealand
| | - Hanna van Waart
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
| | - Simon J Mitchell
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
- Department of Anaesthesia, Auckland City Hospital, Auckland, 1023, New Zealand
| | - Jamie W Sleigh
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
- Department of Anaesthesia, Waikato Hospital, Hamilton, 3240, New Zealand
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Wang Y, Zhao R, Zhu D, Fu X, Sun F, Cai Y, Ma J, Guo X, Zhang J, Xue Y. Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy. Front Neurol 2024; 15:1267349. [PMID: 38419699 PMCID: PMC10899699 DOI: 10.3389/fneur.2024.1267349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Aim The diagnosis of cervical spondylotic myelopathy (CSM) relies on several methods, including x-rays, computed tomography, and magnetic resonance imaging (MRI). Although MRI is the most useful diagnostic tool, strategies to improve the precise and independent diagnosis of CSM using novel MRI imaging techniques are urgently needed. This study aimed to explore potential brain biomarkers to improve the precise diagnosis of CSM through the combination of voxel-based morphometry (VBM) and tensor-based morphometry (TBM) with machine learning techniques. Methods In this retrospective study, 57 patients with CSM and 57 healthy controls (HCs) were enrolled. The structural changes in the gray matter volume and white matter volume were determined by VBM. Gray and white matter deformations were measured by TBM. The support vector machine (SVM) was used for the classification of CSM patients from HCs based on the structural features of VBM and TBM. Results CSM patients exhibited characteristic structural abnormalities in the sensorimotor, visual, cognitive, and subcortical regions, as well as in the anterior corona radiata and the corpus callosum [P < 0.05, false discovery rate (FDR) corrected]. A multivariate pattern classification analysis revealed that VBM and TBM could successfully identify CSM patients and HCs [classification accuracy: 81.58%, area under the curve (AUC): 0.85; P < 0.005, Bonferroni corrected] through characteristic gray matter and white matter impairments. Conclusion CSM may cause widespread and remote impairments in brain structures. This study provided a valuable reference for developing novel diagnostic strategies to identify CSM.
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Affiliation(s)
- Yang Wang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Rui Zhao
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Dan Zhu
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China
| | - Xiuwei Fu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Fengyu Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuezeng Cai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xing Guo
- Department of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Jing Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuan Xue
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin Medical University General Hospital, Tianjin, China
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