Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

Heart rate variability as a predictor of intraoperative autonomic nervous system homeostasis

The aim of the proof-of-concept study is to investigate the level of concordance between the heart rate variability (HRV), the EEG-based Narcotrend Index as a surrogate marker for the depth of hypnosis, and the minimal alveolar concentration (MAC) of the inhalation anesthetic sevoflurane across the entire course of a surgical procedure. This non-blinded cross-sectional study recorded intraoperative HRV, Narcotrend Index, and MAC in 31 male patients during radical prostatectomy using the Da-Vinci robotic-assisted surgical system at Mannheim University Medical Center. The degree of concordance was calculated using repeated measures correlation with the R package (rmcorr) and presented using the rmcorr coefficient (rrm). The Narcotrend Index correlates significantly across all measures with the time-dependent parameter of HRV, the standard deviation of the means of RR intervals (SDNN) (rrm = 0.2; p < 0.001), the frequency-dependent parameters low frequency (LF) (rrm = 0.09; p = 0.04) and the low frequency/high frequency ratio (LF/HF ratio) (rrm = 0.11; p = 0.002). MAC correlated significantly negatively with the time-dependent parameter of heart rate variability, SDNN (rrm = -0.28; p < 0.001), the frequency-dependent parameter LF (rrm = -0.06; p < 0.001) and the LF/HF ratio (rrm = -0.18; p < 0.001) and the Narcotrend Index (rrm = -0.49; p < 0.001) across all measures. HRV mirrors the trend of the Narcotrend Index used to monitor depth of hypnosis and the inhibitory influence of the anesthetic sevoflurane on the autonomic nervous system. Therefore, HRV can provide essential information about the homeostasis of the autonomic nervous system during general anesthesia. DRKS00024696, March 9th, 2021.

Heart rate variability analysis for the prediction of pre-arousal during propofol-remifentanil general anaesthesia: A feasibility study

Accidental awareness during general anaesthesia is a major complication. Despite the routine use of continuous electroencephalographic monitoring, accidental awareness during general anaesthesia remains relatively frequent and constitutes a significant additional cost. The prediction of patients' arousal during general anaesthesia could help preventing accidental awareness and some researchers have suggested that heart rate variability (HRV) analysis contains valuable information about the patient arousal during general anaesthesia. We conducted pilot study to investigate HRV ability to detect patient arousal. RR series and the Bispectral IndexTM (BISTM) were recorded during general anaesthesia. The pre-arousal period T0 was defined as the time at which the BISTM exceeded 60 at the end of surgery. HRV parameters were computed over several time periods before and after T0 and classified as "BISTM<60" or "BISTM≥60". A multivariate logistic regression model and a classification and regression tree algorithm were used to evaluate the HRV variables' ability to detect "BISTM≥60". All the models gave high specificity but poor sensitivity. Excluding T0 from the classification increased the sensitivity for all the models and gave AUCROC>0.7. In conclusion, we found that HRV analysis provided encouraging results to predict arousal at the end of general anaesthesia.

Exploring Heart Rate Variability Biofeedback as a Nonpharmacological Intervention for Enhancing Perioperative Care: A Narrative Review

Heart rate variability biofeedback (HRVBF) is a non-invasive therapeutic technique that aims to regulate variability in heart rate. This intervention has promise in mitigating perioperative stress, a critical factor for surgical patient outcomes. This comprehensive review aimed to explore the current evidence on the perioperative role of HRV biofeedback in improving patient outcomes, reducing perioperative stress, enhancing recovery, and optimizing anaesthesia management. A review of the PubMed and Google Scholar databases was conducted to identify articles focused on HRVBF in relation to the perioperative period. Studies were selected using appropriate keywords in English (MeSH). Ample potential applications of HRVBF in clinical anaesthesia have been identified and proven feasible. It is a non-invasive and an easy method an anaesthesiologists has at its disposal with potential utility in reducing perioperative stress, as a tool of optimization of comorbidities, analgesia supplementation and in predicting catastrophic complications. Although HRVBF has the potential to enhance anaesthesia management and improve patient outcomes, several limitations and challenges must be addressed to maximize its clinical utility. Overcoming these obstacles through research and technological advancements will be crucial for realizing the full benefits of HRVBF in perioperative care.

Effect of different anesthesia depths on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia

BACKGROUND

To analyze the effects of different anesthesia depths on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia, and to provide a basis for clinical application.

METHODS

A total of 111 patients with gastric cancer who were treated with epidural anesthesia combined with general anesthesia were selected as the study subjects, and the patients were randomly divided into group A, group B and group C. The bispectral index (BIS) was maintained by adjusting the infusion speed of anesthetics, the BIS of group A was maintained at 50 ~ 59, the BIS of group B was maintained at 40 ~ 49, and the BIS of group C was maintained at 30 ~ 39. The high-frequency power (HFP), low-frequency power (LFP), total power (TP), mean arterial pressure (MAP), heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP) were measured before anesthesia induction (T1), immediately after intubation (T2), 3 min after intubation (T3), and 6 min after extubation (T4). The cognitive function of the patients was evaluated before and 48 h after surgery.

RESULTS

The HFP, LFP/HFP, TP, HR, DBP and SBP between the three groups at T1 ~ T3 are significantly difference from each other (P < 0.05). There were significant differences in spontaneous breathing recovery time, eye opening time and extubation time among group A, B and C groups, and group B had the lowest spontaneous breathing recovery time, eye opening time and extubation time (P < 0.05). There was no significant difference in the incidence of adverse reactions during anesthesia between the three groups. The cognitive function score of group B was significantly higher than that of group A and group C (P < 0.05).

CONCLUSIONS

BIS maintenance of 40 ~ 49 has little effect on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia, which is helpful for postoperative recovery.

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.

Artificial intelligence and its clinical application in Anesthesiology: a systematic review

PURPOSE

Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.

RESULTS

A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.

CONCLUSION

AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.

Application of Decision Tree Intelligent Algorithm in Data Analysis of Physical Health Test

With the continuous development of China’s cultural industry, people’s health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained.

A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia

Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.