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Wang TC, Li WY, Lai JCY, Kuo TBJ, Yang CCH. Nociception Effect on Frontal Electroencephalogram Waveform and Phase-Amplitude Coupling in Laparoscopic Surgery. Anesth Analg 2024; 138:1070-1080. [PMID: 37428681 DOI: 10.1213/ane.0000000000006609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
BACKGROUND Electroencephalographic pattern changes during anesthesia reflect the nociception-analgesia balance. Alpha dropout, delta arousal, and beta arousal with noxious stimulation have been described during anesthesia; however, data on the reaction of other electroencephalogram signatures toward nociception are scarce. Analyzing the effects of nociception on different electroencephalogram signatures may help us find new nociception markers in anesthesia and understand the neurophysiology of pain in the brain. This study aimed to analyze the electroencephalographic frequency pattern and phase-amplitude coupling change during laparoscopic surgeries. METHODS This study evaluated 34 patients who underwent laparoscopic surgery. The electroencephalogram frequency band power and phase-amplitude coupling of different frequencies were analyzed across 3 stages of laparoscopy: incision, insufflation, and opioid stages. Repeated-measures analysis of variance with a mixed model and the Bonferroni method for multiple comparisons were used to analyze the changes in the electroencephalogram signatures between the preincision and postincision/postinsufflation/postopioid phases. RESULTS During noxious stimulation, the frequency spectrum showed obvious decreases in the alpha power percentage after the incision (mean ± standard error of the mean [SEM], 26.27 ± 0.44 and 24.37 ± 0.66; P < .001) and insufflation stages (26.27 ± 0.44 and 24.40 ± 0.68; P = .002), which recovered after opioid administration. Further phase-amplitude analyses showed that the modulation index (MI) of the delta-alpha coupling decreased after the incision stage (1.83 ± 0.22 and 0.98 ± 0.14 [MI × 10 3 ]; P < .001), continued to be suppressed during the insufflation stage (1.83 ± 0.22 and 1.17 ± 0.15 [MI × 10 3 ]; P = .044), and recovered after opioid administration. CONCLUSIONS Alpha dropout during noxious stimulation is observed in laparoscopic surgeries under sevoflurane. In addition, the modulation index of delta-alpha coupling decreases during noxious stimulation and recovers after the administration of rescue opioids. Phase-amplitude coupling of the electroencephalogram may be a new approach for evaluating the nociception-analgesia balance during anesthesia.
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Affiliation(s)
- Tzu Chun Wang
- From the Department of Anaesthesia, Taitung MacKay Memorial Hospital, Taitung, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei Yi Li
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jerry Cheng-Yen Lai
- Department of Medical Research, Taitung MacKay Memorial Hospital, Taitung, Taiwan
- Master Program in Biomedicine, College of Science and Engineering, National Taitung University, Taitung, Taiwan
| | - Terry B J Kuo
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
- Tsoutun Psychiatric Center, Ministry of Health and Welfare, Nantou, Taiwan
| | - Cheryl C H Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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