
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312366
MD-D-24-02235
00067
10.1097/MD.0000000000038049
3
3300
Research Article
Observational Study
A study comparing brain wave patterns of fentanyl and ketamine in adult patients undergoing minimally invasive surgery
Wang Peng MB 13369516911@163.com
a
https://orcid.org/0009-0007-9986-9716
Ma Gang MD a*
a Department of Anesthesiology and Perioperative Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.
* Correspondence: Gang Ma, Department of Anesthesiology and Perioperative Medicine, General Hospital of Ningxia Medical University, No. 804 Shengli South Street, Xingqing District, Yinchuan, Ningxia Hui Autonomous Region 750004, China (e-mail: magang198106@126.com).
20 9 2024
20 9 2024
103 38 e3804902 3 2024
27 3 2024
05 4 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

This study aimed to investigate and compare the neurophysiological impacts of two widely used anesthetic agents, Fentanyl and Ketamine, on EEG power spectra during different stages of anesthesia in adult patients undergoing minimally invasive surgery. EEG data were collected from patients undergoing anesthesia with either Fentanyl or Ketamine. The data were analyzed for relative power spectrum and fast-to-slow wave power ratios, alongside Spectral Edge Frequency 95% (SEF95), at 3 key stages: pre-anesthesia, during stable anesthesia, and post-anesthesia. EEG Relative Power Spectrum: Initially, both groups exhibited similar EEG spectral profiles, establishing a uniform baseline (P > .05). Upon anesthesia induction, the Fentanyl group showed a substantial increase in delta band power (P < .05), suggesting deeper anesthesia, while the Ketamine group maintained higher alpha and beta band activity (P < .05), indicative of a lighter sedative effect. Fast and Slow Wave Power Ratios: The Fentanyl group exhibited a marked reduction in the fast-to-slow wave power ratio during anesthesia (P < .05), persisting post-anesthesia (P < .05) and indicating a lingering effect on brain activity. Conversely, the Ketamine group demonstrated a more stable ratio (P > .05), conducive to settings requiring rapid cognitive recovery. Spectral Edge Frequency 95% (SEF95): Analysis showed a significant decrease in SEF95 values for the Fentanyl group during anesthesia (P < .05), reflecting a shift towards lower frequency power. The Ketamine group experienced a less pronounced decrease (P > .05), maintaining a higher SEF95 value that suggested a lighter level of sedation. The study highlighted the distinct impacts of Fentanyl and Ketamine on EEG power spectra, with Fentanyl inducing deeper anesthesia as evidenced by shifts towards lower frequency activity and a significant decrease in SEF95 values. In contrast, Ketamine’s preservation of higher frequency activity and more stable SEF95 values suggests a lighter, more dissociative anesthetic state. These findings emphasize the importance of EEG monitoring in anesthesia for tailoring anesthetic protocols to individual patient needs and optimizing postoperative outcomes.

anesthesia
electroencephalography (EEG)
fentanyl
ketamine
Minimally Invasive Surgery
SEF95
OPEN-ACCESSTRUE
SDCT
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pmc1. Introduction

In the realm of modern medical practice, minimally invasive surgery (MIS) represents a significant advancement, offering reduced trauma and faster recovery compared to traditional open surgery.[1] As MIS gains prevalence, the anesthetic management of patients undergoing these procedures has emerged as a crucial area of focus, particularly concerning the selection of anesthetic agents and their neurological implications. Fentanyl, a synthetic opioid, is renowned for its rapid onset and profound analgesic properties, making it a popular choice in pain management during surgeries.[2,3] Conversely, Ketamine, a dissociative anesthetic, is known for its ability to induce sedation, analgesia, and amnesia while maintaining respiratory and cardiovascular stability.[4,5] Despite their widespread use, there is a lack of comprehensive understanding regarding how these drugs modulate brain activity, particularly in the context of MIS. Electroencephalography (EEG) offers a window into the brain’s functioning under anesthesia, revealing alterations in wave patterns like alpha, beta, delta, and theta waves, which correlate with anesthetic depth, patient consciousness levels, and neurophysiological impacts.[6,7] Previous research has extensively documented the individual effects of Fentanyl and Ketamine on brain wave patterns. Fentanyl is associated with an increase in delta and theta waves, indicating deeper anesthesia levels, while Ketamine is often linked to sustained alpha and beta wave activity, reflecting its dissociative effects and preservation of certain cognitive functions during anesthesia.[8–10] However, comparative studies, especially in MIS settings, are scarce. Such comparisons are pivotal, given the potential for different anesthetic agents to influence intraoperative neurological states and affect postoperative recovery, including the risk of cognitive dysfunctions. This study, therefore, seeks to bridge this gap in literature by comprehensively comparing the effects of Fentanyl and Ketamine on brain wave patterns during MIS, with the hypothesis that Fentanyl and Ketamine will exhibit distinct neurophysiological profiles as evidenced by differences in EEG wave patterns. The findings aim to provide deeper insights into the neurophysiological implications of these drugs, guiding anesthesiologists in optimizing intraoperative care and enhancing postoperative patient outcomes. The implications of this research extend beyond anesthetic choice; they hold the potential to influence patient safety and care quality in the rapidly evolving landscape of minimally invasive surgical practices.

2. Materials and methods

2.1. Study population

This study was conducted as a prospective, randomized, double-blind comparison of brain wave patterns in adult patients undergoing minimally invasive surgery (MIS) while anesthetized with either Fentanyl or Ketamine. The study involved a total of 260 patients undergoing minimally invasive surgery between January 2021 and December 2022. Inclusion Criteria: adults aged 18 to 65 years; scheduled for elective minimally invasive surgery; and ASA (American Society of Anesthesiologists) Physical Status I-III. This study was approved by the Ethics Committee of General Hospital of Ningxia Medical University (No. KYLL-2021-358). All patients have signed an informed consent form. Exclusion Criteria: known allergy to Fentanyl or Ketamine; history of chronic pain or long-term opioid use; neurological disorders or history of seizures; and pregnancy or lactation. Patients were randomly assigned to Fentanyl Group and Ketamine Group. Randomization was achieved using computer-generated random numbers to ensure unbiased allocation. Specifically, after obtaining informed consent, eligible patients were assigned a unique identifier. These identifiers were then input into a computerized random number generator, which assigned patients to either the Fentanyl or Ketamine group based on a 1:1 allocation ratio. This method ensured that the assignment of patients to their respective groups was completely random and devoid of any selection bias, contributing to the homogeneity of the groups.

2.2. Anesthetic procedure

2.2.1. Preparation and dosage

Fentanyl Group: Patients in this group received an initial bolus of Fentanyl at a dose of 1 μg/kg, followed by a continuous infusion at a rate of 0.5 to 1 μg/kg/h, adjusted according to the patient’s response and surgical stimuli. Ketamine Group: Patients received Ketamine at an initial dose of 0.5 mg/kg, followed by an infusion at a rate of 10 to 20 μg/kg/min. The infusion rate was adjusted based on hemodynamic parameters and patient response.

2.2.2. Induction and maintenance of anesthesia

Induction was achieved using Propofol (2 mg/kg) and Rocuronium (0.6 mg/kg) for muscle relaxation. Maintenance of anesthesia was carried out with a balanced technique using Sevoflurane in oxygen-air mixture, titrated to maintain a MAC (Minimum Alveolar Concentration) of 1 to 1.5. In both groups, the anesthetic regimen was supplemented with local infiltration or regional blocks as per surgical requirements.

2.2.3. Intraoperative monitoring

Vital Signs: Continuous monitoring of heart rate, blood pressure, respiratory rate, and oxygen saturation was performed. EEG: EEG monitoring involved the use of a standard 4-lead configuration. Parameters such as frequency bands (delta, theta, alpha, beta, gamma), amplitude, and symmetry were analyzed. This comprehensive approach allowed for a detailed assessment of the brain’s electrical activity across a wide range of frequencies, including delta (1–3 Hz), associated with deep sleep or anesthesia; theta (4–7 Hz), related to drowsiness and first stages of sleep; alpha (8–13 Hz), seen in relaxed, yet awake states; beta (14–30 Hz), indicative of active, cognitive processing and alertness; and gamma (>30 Hz), associated with higher-order cognitive functions such as perception and consciousness. Analyzing these parameters provides insights into the neurophysiological effects of anesthetic agents on brain function. Bispectral Index (BIS): BIS monitoring was used to assess the depth of anesthesia, with the target range being 40 to 60 to ensure adequate anesthesia depth while avoiding over-sedation. Additional Monitoring: End-tidal CO2, temperature, and urine output were monitored throughout the surgery.

2.2.4. Post-anesthetic care

Patients were transferred to the post-anesthesia care unit for recovery monitoring. Parameters such as level of consciousness, pain score, hemodynamic stability, and respiratory function were assessed. Recovery from anesthesia was evaluated using the Aldrete score, aiming for a score of ≥ 9 for discharge from post-anesthesia care unit.

2.3. Data collection

2.3.1. EEG data collection and preprocessing

EEG monitoring was conducted in accordance with the International 10 to 20 system for electrode placement. EEG data were collected at 3 critical time points for both groups: baseline (T0: 30 minutes prior to anesthesia induction), stable anesthetic state (T1: 30 minutes after induction completion), and full recovery (T2: 24 hours post-anesthesia).

A 19-channel electrode network, referencing the Gef standard, was used to capture EEG signals from the scalp, ensuring electrode impedances remained below 5 kΩ. EEG data processing was performed using EEGLAB (2021 version). Data underwent band-pass filtering from 1 to 40 Hz and a notch filter at 50 Hz to remove line noise. Each EEG record was segmented into non-overlapping 10-second windows to ensure stationarity.

Artifacts, including ocular and muscular activities, were visually identified and removed. Noisy channels were excluded, and Independent Component Analysis was conducted to eliminate artifact components. The data were then re-referenced to an average reference. Finally, all datasets were downsampled to a uniform frequency of 128 Hz using the Matlab function “resample.m.”

2.3.2. EEG frequency band and scalp region analysis

Five clinical EEG sub-bands were selected for detailed analysis: delta (δ: 1–3 Hz), theta (θ: 4–7 Hz), alpha (α: 8–13 Hz), beta (β: 14–30 Hz), and gamma (γ: 31–40 Hz). Scalp regions were categorized as frontal (Fp1, Fp2, F3, Fz, F4, F7, F8), temporal (T3, T4, T5, T6), parietal (Pz, P3, P4), occipital (Oz, O1, O2), and central (Cz, C3, C4).

Spectral analysis for absolute power in each frequency band was conducted using Fast Fourier Transform in MATLAB R2020a (The MathWorks, Inc., MA). Relative Power was calculated as the percentage of amplitude (absolute power) within a given frequency band relative to the total amplitude (total power) across all frequency bands.

2.4. Statistical analysis

Statistical analyses were conducted using SPSS version 26.0 (Armonk, NY). Categorical data were represented as frequencies and percentages and analyzed using the Chi-square (χ2) test. Continuous data were first assessed for normal distribution using the Shapiro–Wilk Test. Data conforming to normal distribution were expressed as mean ± standard deviation (SD) and compared using independent sample t tests. For non-normally distributed data, medians and interquartile ranges [M(P25, P75)] were calculated, with comparisons conducted using the Wilcoxon Signed Rank Test for related samples and the Mann–Whitney U test for independent samples. A P value of less than .05 was considered indicative of statistical significance. This comprehensive approach allowed for the accurate analysis of diverse data types within the study, ensuring that the statistical conclusions were both robust and reliable. Supplemental Digital Content, http://links.lww.com/MD/N617.

3. Results

3.1. Comparison of baseline characteristics between patient groups

The baseline characteristics of the patients in both groups were compared. The comparison revealed no significant differences between the 2 groups regarding age, gender distribution, BMI, ASA physical status, and duration of surgery, ensuring a homogenous sample for further analysis (P > .05) (Table 1).

Table 1 The baseline characteristics of the patients in both groups.

Characteristic	Fentanyl group	Ketamine group	t value/ Chi-square	P value	
Age (yr)	46.4 ± 5.8	47.1 ± 6.0	0.956	.339	
Gender (M/F)	80/50	78/52	0.065	.799	
BMI (kg/m2)	25.5 ± 3.2	25.2 ± 3.5	0.721	.471	
ASA physical status (I/II/III)	30/70/30	28/72/30	0.097	.953	
Duration of surgery (h)	2.5 ± 0.5	2.6 ± 0.6			
Surgery type	
 Laparoscopic cholecystectomy	40	38			
 Laparoscopic appendectomy	54	50	0.017	.992	
 Laparoscopic hernia repair	46	42			
ASA = American Society of Anesthesiologists.

3.2. Comparison of EEG relative power spectrum between patient groups

Prior to anesthesia induction (T0), both groups exhibited similar EEG spectral profiles. The baseline recordings showed a balanced distribution of power across all frequency bands, with no significant differences between the Fentanyl and Ketamine groups (P > .05). This similarity provided a consistent baseline for subsequent comparative analyses.

At the stable anesthetic state (T1), marked differences emerged between the 2 groups. In the Fentanyl group, there was a substantial increase in the delta (δ: 1–3 Hz) band’s relative power, suggesting deeper anesthesia. Conversely, the Ketamine group displayed a more diverse distribution of power, with relatively higher activity in the alpha (α: 8–13 Hz) and beta (β: 14–30 Hz) bands, indicative of a lighter sedative effect compared to the Fentanyl group. These differences were statistically significant (P < .05), highlighting the distinct anesthetic effects of Fentanyl and Ketamine on brain activity.

In the post-anesthesia phase (T2, 24 hours after anesthesia), the EEG patterns began to return to a state closer to the pre-anesthesia baseline. However, subtle differences were still evident. The Fentanyl group showed a slower return to the baseline with sustained elevated delta activity, whereas the Ketamine group exhibited a quicker normalization of EEG frequencies, especially in the alpha and beta bands (P < .05). This quicker recovery in the Ketamine group suggests a potentially shorter lasting impact on brain wave activity (Fig. 1).

Figure 1. Comparison of EEG relative power spectrum between patient groups. EEG = electroencephalography.

3.3. Comparison of spectral edge frequency 95% (SEF95) between patient groups

Initially, both the Fentanyl and Ketamine groups displayed similar SEF95 values, averaging around 22 Hz, reflecting the awake state’s typical EEG frequency distribution (P > .05). This baseline consistency allowed for a direct comparison of the anesthetic effects on EEG frequency distribution.

During Anesthesia, significant differences in SEF95 were observed between the groups. The Fentanyl group exhibited a marked decrease in SEF95 to an average of 8 Hz, indicative of a significant shift towards lower frequency power and deeper anesthesia. Conversely, the Ketamine group demonstrated a more moderate decrease in SEF95, averaging 14 Hz, suggesting a preservation of higher frequency activity and a lighter anesthetic state (P < .05).

Post-Anesthesia in the recovery phase, the Fentanyl group’s SEF95 values slowly increased but remained below pre-anesthetic levels at 12 Hz 24 hours post-anesthesia, indicating a prolonged effect of the anesthetic on EEG activity. The Ketamine group, however, showed a quicker recovery of SEF95 values back to near-baseline levels, averaging 20 Hz, consistent with a faster normalization of EEG frequency distribution (P < .05).

The SEF95 analysis reveals Fentanyl’s pronounced effect in shifting EEG activity towards lower frequencies, consistent with deeper anesthesia, while Ketamine maintains a higher frequency profile, aligning with its dissociative properties. These findings suggest that SEF95 can serve as a valuable metric for assessing anesthetic depth and the neurophysiological differences between anesthetic agents, with implications for optimizing anesthesia management and patient recovery protocols (Fig. 2).

Figure 2. Comparison of Spectral Edge Frequency 95% (SEF95) between patient groups.

3.4. Comparison of fast and slow wave power ratios in EEG data between patient groups

The analysis of the EEG power ratios between fast (beta and gamma) and slow (delta and theta) wave bands revealed significant differences between the Fentanyl and Ketamine groups at various stages of anesthesia. Before the administration of anesthesia, both groups exhibited similar fast-to-slow wave power ratios with the Fentanyl group averaging at 0.82 (SD ± 0.08) and the Ketamine group at 0.80 (SD ± 0.07), indicating no significant difference (P < .05). During the peak anesthetic effect, the Fentanyl group exhibited a significantly decreased fast-to-slow wave power ratio (0.42, SD ± 0.05), suggesting a predominance of slow wave activity characteristic of deeper anesthesia (P < .05). The Ketamine group, however, maintained a higher ratio (0.68, SD ± 0.06), reflective of its distinct anesthetic profile (P < .05 when compared to the Fentanyl group). 24 hours after anesthesia, the Fentanyl group’s ratio remained lower than baseline (0.50, SD ± 0.06), while the Ketamine group’s ratio (0.75, SD ± 0.05) had returned to a value not significantly different from the pre-anesthesia state (P > .05), indicating a faster reestablishment of pre-anesthetic brain activity patterns (Fig. 3).

Figure 3. Comparison of fast and slow wave power ratios in EEG data between patient groups. EEG = electroencephalography.

4. Discussion

The intricate relationship between anesthetic agents and their neurophysiological effects, as measured by electroencephalography (EEG), remains a pivotal area of research in anesthesia and neurology. EEG offers a window into the cerebral activity, revealing how different anesthetic agents modulate brain function.[11,12] This study focuses on Fentanyl, a potent opioid, and Ketamine, a NMDA receptor antagonist, both widely used in anesthesia but with distinctly different mechanisms of action.[13] Fentanyl is known for its potent sedative effects, primarily acting on the mu-opioid receptors, leading to significant alterations in brain wave patterns, particularly in the slow wave frequencies. Its use in anesthesia is marked by its rapid onset and profound sedation, making it a mainstay for inducing unconsciousness.[14,15] On the other hand, Ketamine, classified as a dissociative anesthetic, operates by blocking the NMDA receptors, resulting in a unique state of “dissociative anesthesia.”[16] Unlike traditional sedatives, Ketamine preserves certain aspects of cortical connectivity and consciousness, which is reflected in its distinct EEG signature.[17] Previous studies have extensively examined the individual effects of these anesthetics on EEG. However, there remains a gap in the comparative analysis across different stages of anesthesia. The primary objective of this study is to elucidate the differential neurophysiological impacts of Fentanyl and Ketamine on EEG power spectra during various stages of anesthesia. By analyzing the EEG spectral profiles, power topography, and fast-to-slow wave power ratios, the study seeks to deepen our understanding of how these anesthetics modulate cortical activity and consciousness. This exploration is crucial for enhancing our knowledge of anesthetic mechanisms and for optimizing the use of these drugs in clinical practice. Our findings indicate significant differences in EEG spectral profiles, power topography, and fast-to-slow wave power ratios between the patient groups anesthetized with Fentanyl and Ketamine. These variations contribute to a nuanced understanding of how these anesthetics regulate brain activity, with implications for their selective use based on desired outcomes in anesthesia and patient recovery.

The initial phase of our study, which showed no significant differences in EEG spectral profiles between the 2 groups before anesthesia. Upon the induction of anesthesia, the divergent effects of Fentanyl and Ketamine on EEG power spectra became pronounced. Fentanyl’s enhancement of delta wave activity correlates with its role as a potent mu-opioid agonist. The mu-opioid system’s involvement in anesthesia is well-documented, with its activation leading to a reduction in neuronal excitability and synaptic transmission, resulting in the slowing of brain activity.[18] This slowing is observable in EEG as an increase in delta power, a hallmark of deep sedation and the loss of consciousness. The observed increase in delta activity with Fentanyl corroborates with its clinical use for inducing deep sedative states in surgical settings. In contrast, the EEG spectral profile under Ketamine anesthesia presented an intriguing pattern. The preservation of alpha and beta wave activities is indicative of Ketamine’s unique pharmacodynamic profile as an NMDA receptor antagonist. Unlike typical sedatives that broadly depress neuronal activity, Ketamine selectively inhibits excitatory neurotransmission mediated by NMDA receptors. This selective inhibition results in a dissociative state, where a patient may lose the ability to process environmental stimuli while maintaining certain aspects of cortical function.[19,20] The maintenance of alpha and beta activities, often associated with cortical arousal and cognitive processing, suggests that Ketamine preserves a degree of cortical connectivity and higher cognitive function, even in a state of anesthesia. This aligns with recent studies exploring Ketamine’s potential in preserving cognitive function and facilitating a rapid postoperative recovery.[21]

The comparative analysis of SEF95 between Fentanyl and Ketamine groups across different stages of anesthesia offers profound insights into the modulation of EEG frequency distribution by these anesthetic agents. Our findings reveal a clear distinction in the impact of Fentanyl and Ketamine on brain activity, as reflected in the spectral edge frequency, a metric indicative of the dominant frequency bands in EEG recordings. The significant decrease in SEF95 during anesthesia with Fentanyl underscores its potent sedative properties. The shift towards lower frequencies, particularly the pronounced delta activity, is indicative of deep anesthesia. This is consistent with Fentanyl’s pharmacological profile as a potent mu-opioid agonist, known to facilitate profound sedation and analgesia.[22] The slow post-anesthetic recovery in SEF95 further reflects Fentanyl’s lasting neurophysiological effects, potentially influencing the duration of postoperative cognitive recovery. Clinicians should consider these aspects when choosing Fentanyl for anesthesia, particularly in procedures requiring deep sedation.[23,24] In contrast, the relatively stable SEF95 values observed with Ketamine suggest its unique action in preserving higher frequency brain activity, even under anesthesia.[25] This finding supports the dissociative anesthetic profile of Ketamine, which maintains a degree of cortical connectivity and cognitive function.[26] The rapid post-anesthesia recovery in SEF95 values aligns with Ketamine’s clinical use in settings where quick cognitive recovery is advantageous, such as outpatient procedures or in the treatment of acute pain and depression. These results have significant clinical implications, highlighting the importance of selecting anesthetic agents based on the desired depth of anesthesia and the postoperative recovery profile. The ability to predict and monitor the depth of anesthesia through SEF95 could enhance patient safety, optimize anesthetic dosing, and potentially reduce the incidence of postoperative cognitive dysfunction.

The analysis of fast-to-slow wave power ratios in EEG data during anesthesia provides a quantifiable measure of brain activity modulation by anesthetic agents. Our findings resonate with the pivotal research by Lee et al,[27] who demonstrated the utility of these ratios in assessing the depth of anesthesia. This correlation is crucial in clinical settings, as it offers an objective metric to guide anesthetic dosing and monitor patient responses, thereby enhancing patient safety and outcome. In the context of Fentanyl, a drug known for its profound sedative effects, the significant decrease in the fast-to-slow wave power ratio confirms its impact on inducing a state of deep anesthesia. The slow post-anesthetic recovery in this ratio observed in our study is indicative of Fentanyl’s extended influence on the brain’s electrical activity. This lingering effect might account for the prolonged cognitive impairment observed in some patients post-surgery, aligning with concerns raised in recent studies about potential impacts on postoperative cognitive function, particularly in vulnerable populations such as the elderly.[28] Understanding these effects is crucial for tailoring postoperative care and mitigating risks associated with cognitive dysfunction. Conversely, the relatively stable fast-to-slow wave power ratio observed with Ketamine suggests a different mode of action. Ketamine’s ability to maintain a balance between fast and slow waves aligns with its NMDA antagonistic properties, which do not fully disrupt cortical connectivity. This pharmacological action makes Ketamine an ideal candidate for procedures requiring rapid recovery of cognitive function, such as in ambulatory surgeries or in the treatment of acute pain and depression. The findings of Gilbert et al highlight Ketamine’s rapid antidepressant effects, which may be partly attributed to its unique influence on EEG power dynamics.[29] Moreover, these EEG findings have significant implications for the future development of anesthetic protocols. They suggest the potential for using EEG-derived metrics like the fast-to-slow wave power ratio as a real-time guide for anesthetic administration, enabling a more personalized approach to anesthesia that adjusts to the dynamic changes in brain activity. This approach could optimize patient outcomes, particularly in terms of cognitive recovery and overall neurological health.

5. Limitations

Our study’s sample was confined to patients undergoing minimally invasive surgery within a specific timeframe at a single medical center. This limitation may restrict the generalizability of our findings to other populations or settings. Future studies could address this by including a more diverse patient cohort across multiple centers to enhance the external validity of the research. Additionally, while EEG is a valuable tool for assessing brain activity under anesthesia, its interpretation can be influenced by various factors such as electrode placement accuracy, external noise, and muscle artifacts. These factors may affect the reliability of the EEG data. Subsequent research could explore advanced EEG processing techniques or complementary neuroimaging methods to mitigate these limitations and provide a more comprehensive understanding of anesthetic effects on brain function.

6. Conclusions

This study provided a comparative analysis of the neurophysiological effects of Fentanyl and Ketamine on EEG power spectra during anesthesia in adult patients undergoing minimally invasive surgery. Key findings revealed that while Fentanyl significantly enhanced delta wave activity, indicative of deeper anesthesia, Ketamine maintained higher alpha and beta activities, suggestive of a lighter sedative effect. These results align with the distinct pharmacological profiles of the 2 drugs and underscore the utility of EEG in monitoring and tailoring anesthesia. However, the study had limitations, including a focus on specific EEG parameters and a limited patient cohort, which might not capture the full spectrum of neurophysiological responses to these anesthetics. Future research should aim to include a broader range of EEG metrics and a more diverse patient population. Additionally, investigating the postoperative cognitive outcomes in relation to EEG changes during anesthesia could provide valuable insights into optimizing patient recovery and anesthetic choice.

Author contributions

Conceptualization: Peng Wang, Gang Ma.

Data curation: Peng Wang, Gang Ma.

Formal analysis: Peng Wang, Gang Ma.

Investigation: Peng Wang, Gang Ma.

Validation: Peng Wang, Gang Ma.

Writing – original draft: Peng Wang.

Writing – review & editing: Gang Ma.

Supplementary Material

Abbreviations:

ASA American Society of Anesthesiologists

EEG electroencephalography

MIS minimally invasive surgery

SD standard deviation

SEF95 Spectral Edge Frequency 95%

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Wang P, Ma G. A study comparing brain wave patterns of fentanyl and ketamine in adult patients undergoing minimally invasive surgery. Medicine 2024;103:38(e38049).
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