EEG spectral entropy, heart rate, photoplethysmography and motor responses to skin incision during sevoflurane anaesthesia

Preserving shape details of pulse signals for video-based blood pressure estimation

In recent years, imaging photoplethysmograph (iPPG) pulse signals have been widely used in the research of non-contact blood pressure (BP) estimation, in which BP estimation based on pulse features is the main research direction. Pulse features are directly related to the shape of pulse signals while iPPG pulse signals are easily disturbed during the extraction process. To mitigate the impact of pulse feature distortion on BP estimation, it is necessary to eliminate interference while retaining valuable shape details in the iPPG pulse signal. Contact photoplethysmograph (cPPG) pulse signals measured at rest can be considered as the undisturbed reference signal. Transforming the iPPG pulse signal to the corresponding cPPG pulse signal is a method to ensure the effectiveness of shape details. However, achieving the required shape accuracy through direct transformation from iPPG to the corresponding cPPG pulse signals is challenging. We propose a method to mitigate this challenge by replacing the reference signal with an average cardiac cycle (ACC) signal, which can approximately represent the shape information of all cardiac cycles in a short time. A neural network using multi-scale convolution and self-attention mechanisms is developed for this transformation. Our method demonstrates a significant improvement in the maximal information coefficient (MIC) between pulse features and BP values, indicating a stronger correlation. Moreover, pulse signals transformed by our method exhibit enhanced performance in BP estimation using different model types. Experiments are conducted on a real-world database with 491 subjects in the hospital, averaging 60 years of age.

Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder

Opioid use disorder (OUD) is one of the most pressing public health problems of the past decade, with over eighty thousand overdose related deaths in 2021 alone. Digital technologies to measure and respond to disease states encompass both on- and off-body sensors. Such devices can be used to detect and monitor end-user physiologic or behavioral measurements (i.e. digital biomarkers) that correlate with events of interest, health, or pathology. Recent work has demonstrated the potential of digital biomarkers to be used as a tools in the prevention, risk mitigation, and treatment of opioid use disorder (OUD). Multiple physiologic adaptations occur over the course of opioid use, and represent potential targets for digital biomarker based monitoring strategies. This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use. Technologies to detect opioid administration, withdrawal, hyperalgesia and overdose will be reviewed. Driven by empirically derived algorithms, these technologies have important implications for supporting the safe prescribing of opioids, reducing harm in active opioid users, and supporting those in recovery from OUD.

Fully Transparent, Ultrathin Flexible Organic Electrochemical Transistors with Additive Integration for Bioelectronic Applications

Optical transparency is highly desirable in bioelectronic sensors because it enables multimodal optical assessment during electronic sensing. Ultrathin (<5 µm) organic electrochemical transistors (OECTs) can be potentially used as a highly efficient bioelectronic transducer because they demonstrate high transconductance during low-voltage operation and close conformability to biological tissues. However, the fabrication of fully transparent ultrathin OECTs remains a challenge owing to the harsh etching processes of nanomaterials. In this study, fully transparent, ultrathin, and flexible OECTs are developed using additive integration processes of selective-wetting deposition and thermally bonded lamination. These processes are compatible with Ag nanowire electrodes and conducting polymer channels and realize unprecedented flexible OECTs with high visible transmittance (>90%) and high transconductance (≈1 mS) in low-voltage operations (<0.6 V). Further, electroencephalogram acquisition and nitrate ion sensing are demonstrated in addition to the compatibility of simultaneous assessments of optical blood flowmetry when the transparent OECTs are worn, owing to the transparency. These feasibility demonstrations show promise in contributing to human stress monitoring in bioelectronics.

Intraoperative Assessment of Surgical Stress Response Using Nociception Monitor under General Anesthesia and Postoperative Complications: A Narrative Review

We present a narrative review focusing on the new role of nociception monitor in intraoperative anesthetic management. Higher invasiveness of surgery elicits a higher degree of surgical stress responses including neuroendocrine-metabolic and inflammatory-immune responses, which are associated with the occurrence of major postoperative complications. Conversely, anesthetic management mitigates these responses. Furthermore, improper attenuation of nociceptive input and related autonomic effects may induce increased stress response that may adversely influence outcome even in minimally invasive surgeries. The original role of nociception monitor, which is to assess a balance between nociception caused by surgical trauma and anti-nociception due to anesthesia, may allow an assessment of surgical stress response. The goal of this review is to inform healthcare professionals providing anesthetic management that nociception monitors may provide intraoperative data associated with surgical stress responses, and to inspire new research into the effects of nociception monitor-guided anesthesia on postoperative complications.

Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection

Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p < 0.05) except total area feature (p > 0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen’s kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring.

Photoplethysmogram Analysis and Applications: An Integrative Review

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers

OBJECTIVE

To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation.

METHODS

We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation.

RESULTS

Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG.

CONCLUSION

Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception.

SIGNIFICANCE

The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

Intraoperative Nociception Monitoring

Nociception refers to the process of encoding and processing noxious stimuli. Its monitoring can have potential benefits. Under anesthesia, nociceptive signals are continuously generated to cause involuntary effects on the autonomic nervous system, reflex movement, and stress response. Most available systems depend on the identification and measurement of these indirect effects to indicate nociception-antinociception balance. Despite advances in monitoring technology and availability, their limitations presently override their benefits. Hence, their utility and applicability in present-day anesthesia care is uncertain. Future technologies might allow automated closed-loop multimodal anesthesia systems, which includes the components of hypnosis and analgesic balance for a patient.

Using the Perfusion Index to predict changes in the depth of anesthesia in children compared with the A-line Autoregression Index: an observational study

BACKGROUND

We investigated the performance of the Perfusion Index (PI) derived from pulse oximetry waveform as a tool for assessment of anesthetic depth in comparison with A-line Autoregression Index (AAI) derived from analysis of Middle-Latency Auditory Evoked Potentials (MLAEP) waveform integrated by aepEXplus monitor in children receiving sevoflurane anesthesia for tonsillectomy.

METHODS

Forty-one patients (4-12 years old) were included in this study. The PI and AAI were recorded simultaneously every minute during different stages of anesthesia delivery. The statistical tests included descriptive analysis, significance tests, correlation tests, and Receiver Operating Characteristic (ROC) curve. The AAI served as a reference.

RESULTS

The PI significantly decreased during light anesthesia and recovery, and significantly increased during deeper planes of anesthesia, with an inverse mirror-image relationship with the AAI. A negative correlation of low to moderate degree was detected between PI and AAI during the study (p > 0.05), that reached a statistical significance at the 5th minute during sevoflurane mask induction (r = -0.457, p = 0.008). ROC analysis at an AAI < 25 extracted the best cut-off value for PI before intubation as 1.48 (AUC = 0.698 [0.537-0.859], 94.4% sensitivity, 44.5% specificity) and at 10-minute intraoperatively as 2.4 (AUC = 0.537 [0.354-0.721], 91.7% sensitivity, 31% specificity). During recovery, at an AAI ≥ 50, the best cutoff was 1.82 (AUC = 0.661 [0.46-0.863], 100% sensitivity and 50% specificity) 2 minutes before spontaneous eye opening.

CONCLUSIONS

Compared with the AAI, the PI can track changes in depth of anesthesia in pediatric patients undergoing tonsillectomy under sevoflurane anesthesia.

TRIAL REGISTRATION

Clinical Trials. Gov. Identifier: NCT03412214.

A Preoperative Predictive Model of Lower Lumbar Spine Instability Based on Three-Dimensional Computed Tomography: A Retrospective Case-Control Pilot Study

OBJECTIVE

This study aimed to build a predictive model of lower lumbar instability.

METHODS

This retrospective study included 199 patients. Patients were divided into the lower lumbar instability group (LLIG) (n = 98) and lower lumbar stability group (LLSG) (n = 101). All participants of LLIG were recruited over a 2-year period (2015-2017) from the patients who accept lumbar surgery at the First Hospital of Jilin University. The LLSG was selected from outpatients who had underwent lumbar spine computed tomography (CT) and Flexion and extension radiographs (FER) at the First Hospital of Jilin University from 2015 to 2017. Several lower lumbar parameters were measured, including Lordosis angle (LA), intervertebral height (IH), ratio of anterior height to posterior height (APR), angle between endplate and anterior edge of vertebral body (AEPVa), sagittal slip ratio (SSR), and angle between the upper endplate and z-axis on sagittal plane (AUEZS). These parameters were keyed into the SPSS software to create a predictive model for classification. Sensitivity, specificity, predictive accuracy, and Kappa value were used to evaluate the predictive model.

RESULTS

Compared with LLSG, the LA of LLIG decreased by 3.49° (126.54° vs 130.3°). Similarly, the IH of LLIG decreased by 1.23°mm, 1.66°mm, and 0.71°mm at L3-4, L4-5, and L5-S1. Compared with LLSG, the SSR of LLIG is higher at L3-4, L4-5, and L5-S1 (0.54 vs 0.51, 0.57 vs 0.46, and 0.59 vs 0. 47). Moreover, the APR of LLIG is higher than those of LLSG at L3-4, L4-5, and L5-S1 (1.97 vs 1.81, 2.40 vs 1.97, and 2.69 vs 2.26). The LLIG has bigger AEPVa than LLIG at L3-4, L4-5, and L5-S1. Compared with LLSG, the AUEZS of LLIG is bigger at L3-4 (91.75° vs 90.81°) and smaller at L4-5 and L5-S1(84.63° vs 85.85° and 73.27° vs 75.01°). The SSR (L4) show highest predictive accuracy (83%) when every parameter was fed to LDA classifier to generate a univariate model. All parameters represent a statistically significant difference (P < 0.05) between LLSG and LLIG. The model including LA, APR (L5-S1), IH (L4-5), SSR (L5), AUEZS (L5) has highest predictive accuracy of 88.2%. The sensitivity, specificity, and Kappa value are 88.7%, 93.1%, and 0.77.

CONCLUSION

The predictive model has good classification performance and can be an auxiliary tool for clinicians to evaluate lumbar instability in preoperative patients with severe pain aggravated by lumbar movement.

On Driver Behavior Recognition for Increased Safety: A Roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

Photoplethysmography Response to Laryngeal Mask Airway Insertion during Propofol-Remifentanil Anethesia

The present study was aimed to evaluate the abilities of photoplethysmography (PPG)-derived parameters, including sample entropy of PPG (SampEn), amplitude of PPG (PPGA), pulse beat interval (PBI) and diastolic interval (DI) extracted by nonlinear or linear methods, to monitor the balance between nociception and antinociception. 26 ASA I or II patients were randomized into one of the three groups to receive a remifentanil effect-compartment target controlled infusion (Ce_remi) of 1, 3 and 5 ng/ml and an effect-compartment target controlled propofol infusion (Ce_prop) to keep the state entropy (SE) at 50 (40~60). Laryngeal mask airway (LMA) insertion was applied as a noxious stimulus. The percentage of change in SampEn (△SampEn, AUC=0.896), PBI (△PBI, AUC=0.896) and DI (ΔDI, AUC=0.972), but not in PPGA (△PPGA, AUC=0.667), were statistically excellent in discriminating low Ce_remi (1 ng/ml) from higher Ce_remi (3 and 5 ng/ml). Additionally, the prediction probabilities (P_k) values of △SampEn, ΔPBI and ΔDI were high as well with 0.795, 0.754 and 0.813 for discriminating Ce_remi. These results demonstrated that nonlinear and linear parameters of SampEn, PBI and DI had strong dependency on Ce_remi in response to LMA insertion and could provide nociceptive information during propofol-remifentanil anesthesia. This indicated that PPG-derived parameters were potential to develop the clinical assessment of nociception-antinociception balance under general anesthesia.

Differentiation of skin incision and laparoscopic trocar insertion via quantifying transient bradycardia measured by electrocardiogram

Most surgical procedures involve structures deeper than the skin. However, the difference in surgical noxious stimulation between skin incision and laparoscopic trocar insertion is unknown. By analyzing instantaneous heart rate (IHR) calculated from the electrocardiogram, in particular the transient bradycardia in response to surgical stimuli, this study investigates surgical noxious stimuli arising from skin incision and laparoscopic trocar insertion, and their difference. Thirty-five patients undergoing laparoscopic cholecystectomy were enrolled in this prospective observational study. Sequential surgical steps including umbilical skin incision (11 mm), umbilical trocar insertion (11 mm), xiphoid skin incision (5 mm), xiphoid trocar insertion (5 mm), subcostal skin incision (3 mm), and subcostal trocar insertion (3 mm) were investigated. IHR was derived from electrocardiography and calculated by the modern time-varying power spectrum. Similar to the classical heart rate variability analysis, the time-varying low frequency power (tvLF), time-varying high frequency power (tvHF), and tvLF-to-tvHF ratio (tvLHR) were calculated. Prediction probability (P_K) analysis and global pointwise F-test were used to compare the statistical performance between indices and the heart rate readings from the patient monitor. Analysis of IHR showed that surgical stimulus elicits a transient bradycardia, followed by the increase of heart rate. Transient bradycardia is more significant in trocar insertion than skin incision (p < 0.001 for tvHF). The IHR change quantifies differential responses to different surgical intensity. Serial P_K analysis demonstrates de-sensitization in skin incision, but not in laparoscopic trocar insertion. Quantitative indices present the transient bradycardia introduced by noxious stimulation. The results indicate different effects between skin incision and trocar insertion.

Postoperative Pain Assessment Model Based on Pulse Contour Characteristics Analysis

This study aims to develop a new postoperative pain assessment model based on pulse contour analysis and to evaluate its effectiveness in postoperative pain assessment. We derived candidate features from photoplethysmography (PPG) and developed an assessment model based on multiple logistic regressions with a combination of features. This study also includes investigations into the optimal unit of analysis and number of features. For model development, PPGs obtained from 78 surgical patients with a six-min duration in pre- and post-operation conditions, including a training set of 56 pairs and a test set of 22 pairs, were used. We tested models with 5, 10, 20, 30, 40, 50, 60, 70, and 80 beats as an analysis unit, and with 1 to 8 of features for optimization, then determined 20 beats and three features to be the simplest optimal unit of analysis and number of features, respectively. The selected features were RMSSD-ACV_onset/ACA_bl, AV-A_sys/A_total, and SD-RS, where RMSSD-ACV_onset/ACA_bl is the root mean square of the successive difference of the ratio of pulse onset amplitude to the pulse onset-to-peak amplitude, AV-A_sys/A_total is the average value of a normalized systolic area of a pulse with a total pulse area, and SD-RS is the standard deviation of a rising slope of a pulse. The accuracy (AC) and the area under the curve (AUC) of the proposed model were 0.793 and 0.872 in the development set (N = 56), respectively, which were superior to those of SPI (AC: 0.643, AUC: 0.716) and ANI (AC: 0.633 AUC: 0.671). In the test set (N = 22), the AC and AUC of the proposed model were 0.712 and 0.808, respectively, which were superior to those of SPI (AC: 0.640, AUC: 0.709) and ANI (AC: 0.640, AUC: 0.680).

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

Introduction

Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and a photoplethysmograph (PPG) that make them unsuitable for true wearable applications. Therefore, developing a single PPG-based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful.

Methods

The University of Queensland vital sign dataset (online database) was accessed to extract raw PPG signals and its corresponding reference BPs (systolic BP and diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5 s for each) were extracted, preprocessed, and normalised in both width and amplitude. Three most significant pulse features (pulse area, pulse rising time, and width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (regression tree, multiple linear regression (MLR), and support vector machine (SVM)). A 10-fold cross-validation was applied to obtain overall BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (normotensive, hypertensive, and hypotensive). Finally, they were compared with the ISO standard for noninvasive BP device validation (average difference no greater than 5 mmHg and SD no greater than 8 mmHg).

Results

In terms of overall estimation accuracy, the regression tree achieved the best overall accuracy for SBP (mean and SD of difference: −0.1 ± 6.5 mmHg) and DBP (mean and SD of difference: −0.6 ± 5.2 mmHg). MLR and SVM achieved the overall mean difference less than 5 mmHg for both SBP and DBP, but their SD of difference was >8 mmHg. Regarding the estimation accuracy in each BP categories, only the regression tree achieved acceptable ISO standard for SBP (−1.1 ± 5.7 mmHg) and DBP (−0.03 ± 5.6 mmHg) in the normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories.

Conclusion

This study developed and compared three machine learning algorithms to estimate BPs using PPG only and revealed that the regression tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the regression tree algorithm achieved acceptable measurement accuracy only in the normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories.

Postoperative Pain Assessment Indices Based on Photoplethysmography Waveform Analysis

The purpose of this study was to derive parameters that might reflect postoperative pain from photoplethysmography (PPG) and verify the derived parameters in postoperative pain assessment. We obtained preoperative and postoperative PPG and 100-mm visual analog scale (VAS) from 65 surgical patients and extracted a total of 51 PPG morphology-based parameters and their normalized parameters from these PPGs obtained. Pain discrimination performances of these derived parameters were assessed by statistical analyses, including Wilcoxon signed rank test with Bonferroni correction, classification accuracy based on logistic regression, and 4-fold cross validation. After comparing these parameters derived from PPG in pre- and post-operative conditions, statistically significant difference was found in 36 of the 51 parameters. Using logistic classification, dynamic between-pulse parameters such as normalized systolic amplitude variation and normalized diastolic amplitude variation showed better pain classification performance than the static within-pulse parameters. VAS score was 0 in every pre-operation condition, but >60 VAS was observed in the post-operative condition. Systolic peak amplitude variation normalized by PPG AC amplitude showed the best performance in classifying post-operative pain, with accuracy, sensitivity, specificity, and positive predictivity values of 79.5, 74.0, 86.0, and 84.5%, respectively. These results are superior to those of the surgical pleth index (SPI, GE Healthcare, Chicago, IL, United States) at 65.9, 65.9, 66.5, and 66.5%, respectively.

Sleep-wake classification via quantifying heart rate variability by convolutional neural network

OBJECTIVE

Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series.

APPROACH

We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities.

MAIN RESULTS

On our private database of 27 participants, our accuracy, sensitivity, specificity, and [Formula: see text] values for predicting the wake stage are [Formula: see text], 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics.

SIGNIFICANCE

This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.

A review on wearable photoplethysmography sensors and their potential future applications in health care

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

Measurement of area difference ratio of Photoplethysmographic pulse wave in patients with pre-eclampsia

Background

Preeclampsia (PE) is associated with an increase in maternal arterial stiffness, which may be reflected by photoplethysmography (PPG) of the pulse wave. The aim of this study was to investigate area difference ratio (ADR), a novel parameter derived from PPG, in women with and without preeclampsia.

Methods

Patients with and without preeclampsia in the third trimester were enrolled. All patients had photoplethysmography of the pulse wave assessed. ADR was compared between the two groups.

Results

Seventy-two patients in the third trimester of gestation, of which 36 had preeclampsia and 36 did not, were enrolled. The ADR was lower in the preeclampsia group vs. the non-preeclampsia group (0.725 [IQR 0.681–0.779] vs. 0.752 [IQR 0.717–0.910], P < 0.01).

Conclusions

Measuring the ADR through analyzing PPG of the pulse wave may be a useful diagnostic tool in patients with preeclampsia.

Photoplethysmography and Heart Rate Variability for the Diagnosis of Preeclampsia

BACKGROUND

The goal of this study was to determine a set of timing, shape, and statistical features available through noninvasive monitoring of maternal electrocardiogram and photoplethysmography that identifies preeclamptic patients.

METHODS

Pregnant women admitted to Labor and Delivery were monitored with pulse oximetry and electrocardiogram for 30 minutes. Photoplethysmogram features and heart rate variability were extracted from each data set and applied to a sequential feature selection algorithm to discriminate women with preeclampsia with severe features, from normotensive and hypertensive controls. The classification boundary was chosen to minimize the expected misclassification cost. The prior probabilities of the misclassification costs were assumed to be equal.

RESULTS

Thirty-seven patients with clinically diagnosed preeclampsia with severe features were compared with 43 normotensive controls; all were in early labor or beginning induction. Six variables were used in the final model. The area under the receiver operating characteristic curve was 0.907 (standard error [SE] = 0.004) (sensitivity 78.2% [SE = 0.3%], specificity 89.9% [SE = 0.1%]) with a positive predictive value of 0.883 (SE = 0.001). Twenty-eight subjects with chronic or gestational hypertension were compared with the same preeclampsia group, generating a model with 5 features with an area under the curve of 0.795 (SE = 0.007; sensitivity 79.0% [SE = 0.2%], specificity 68.7% [SE = 0.4%]), and a positive predictive value of 0.799 (SE = 0.002).

CONCLUSIONS

Vascular parameters, as assessed noninvasively by photoplethysmography and heart rate variability, may have a role in screening women suspected of having preeclampsia, particularly in areas with limited resources.

The Effect of Tracheal Intubation-Induced Autonomic Response on Photoplethysmography

Introduction. Intraoperative stress responses and postoperative pain can be monitored using photoplethysmography (PPG). PPG signal has two components, AC and DC. Effects of noxious stimuli-induced stress responses have not been studied on the DC component of PPG. The aim of this study was to investigate the effect of a known noxious stimulus (endotracheal intubation) on both the AC and DC components of PPG. Methods. 15 surgical patients having general anesthesia were enrolled into this clinical study. PPG was recorded electronically from a pulse oximeter. Maximum changes in the AC and DC components of the PPG and pulse rate were determined in response to endotracheal intubation from high frequency (62.5 Hz) PPG recordings. Results. Endotracheal intubation-induced autonomic stress response resulted in a significant decrease in the AC component of the PPG and an increase in pulse rate in every subject (p < 0.05 for all). The decrease in the AC component of the PPG was 50 ± 12% (p < 0.05) and the increase in pulse rate was 26 ± 10 bpm (p < 0.05). The response of the DC component was variable (p = NS). Conclusion. Endotracheal intubation-induced stress response resulted in a significant and consistent change in the AC, but not the DC component of the PPG. This trial is registered with ClinicalTrials.gov Identifier NCT03032939.

Monitoring of nociception, reality or fiction?

There are currently various projects underway that attempt to monitor the nociceptive responses caused by surgical stress and ensure patients the best analgesic conditions. The systemic response to surgical stress has repercussions in the postoperative period, such as worse pain control, delayed recovery, greater complications, longer stay in resuscitation and hospital units, and increased healthcare costs. However, treatment with higher doses of opioids than necessary may lead to slower awakening, increased drowsiness and adverse effects, as well as situations of postoperative opioid-induced hyperalgesia. There are 2 large groups of nociceptive monitoring according to the origin of the theoretical objective of monitoring response to the stimulus, that may derive from changes in the electroencephalogram or the response of the autonomic nervous system.

Spectral entropy in monitoring anesthetic depth

Monitoring the brain response to hypnotics in general anesthesia, with the nociceptive and hemodynamic stimulus interaction, has been a subject of intense investigation for many years. Nowadays, monitors of depth of anesthesia are based in processed electroencephalogram by different algorithms, some of them unknown, to obtain a simplified numeric parameter approximate to brain activity state in each moment. In this review we evaluate if spectral entropy suitably reflects the brain electric behavior in response to hypnotics and the different intensity nociceptive stimulus effect during a surgical procedure.

ConceFT for Time-Varying Heart Rate Variability Analysis as a Measure of Noxious Stimulation During General Anesthesia

OBJECTIVE

Heart rate variability (HRV) offers a noninvasive way to peek into the physiological status of the human body. When this physiological status is dynamic, traditional HRV indices calculated from power spectrum do not resolve the dynamic situation due to the issue of nonstationarity. Clinical anesthesia is a typically dynamic situation that calls for time-varying HRV analysis. Concentration of frequency and time (ConceFT) is a nonlinear time-frequency (TF) analysis generalizing the multitaper technique and the synchrosqueezing transform. The result is a sharp TF representation capturing the dynamics inside HRV. Companion indices of the commonly applied HRV indices, including time-varying low-frequency power (tvLF), time-varying high-frequency power, and time-varying low-high ratio, are considered as measures of noxious stimulation.

METHODS

To evaluate the feasibility of the proposed indices, we apply these indices to study two different types of noxious stimulation, the endotracheal intubation and surgical skin incision, under general anesthesia. The performance was compared with traditional HRV indices, the heart rate reading, and indices from electroencephalography.

RESULTS

The results indicate that the tvLF index performs best and outperforms not only the traditional HRV index, but also the commonly used heart rate reading.

CONCLUSION

With the help of ConceFT, the proposed HRV indices are potential to provide a better quantification of the dynamic change of the autonomic nerve system.

SIGNIFICANCE

Our proposed scheme of time-varying HRV analysis could contribute to the clinical assessment of analgesia under general anesthesia.

Effect of Pre- and Postoperative Phenylbutazone and Morphine Administration on the Breathing Response to Skin Incision, Recovery Quality, Behavior, and Cardiorespiratory Variables in Horses Undergoing Fetlock Arthroscopy: A Pilot Study

This prospective blinded randomized study aimed to determine whether the timing of morphine and phenylbutazone administration affects the breathing response to skin incision, recovery quality, behavior, and cardiorespiratory variables in horses undergoing fetlock arthroscopy. Ten Standardbred horses were premedicated with acepromazine (0.04 mg kg⁻¹ IM) and romifidine (0.04 mg kg⁻¹ IV). Anesthesia was induced with diazepam (0.05 mg kg⁻¹) and ketamine (2.2 mg kg⁻¹) IV at T0. Horses in group PRE (n = 5) received morphine (0.1 mg kg⁻¹) and phenylbutazone (2.2 mg kg⁻¹) IV after induction and an equivalent amount of saline after surgery. Horses in group POST (n = 5) received the inversed treatment. Anesthesia was maintained with isoflurane 2% in 100% oxygen. Hypotension (mean arterial pressure <60 mmHg) was treated with dobutamine. All horses breathed spontaneously. Dobutamine requirements, respiratory rate (f_R), heart rate (HR), mean arterial blood pressure, end-tidal CO₂, inspired (_i) and expired (_e) tidal and minute volume (V_T and V˙E), inspiratory time (IT), and the inspiratory gas flow (V_Ti/IT) were measured every 5 min. Data were averaged during four 15 min periods before (P1 and P2) and after the incision (P3 and P4). Serial blood–gas analyses were also performed. Recoveries were unassisted, video recorded, and scored by three anesthetists blinded to the treatment. The postoperative behavior of the horses (25 demeanors), HR, and f_R were recorded at three time points before induction (T0–24 h, T0–12 h, and T0–2 h) and six time points after recovery (TR) (TR + 2, 4, 6, 12, 24, 48 h). Data were compared between groups using a Wilcoxon test and within groups using a Friedman test or a Kruskal–Wallis signed-rank test when applicable. Tidal volumes (V_Te and V_Ti) were higher in PRE than in POST during all the considered periods but the difference between groups was only significant during P2 (V_Te in mL kg⁻¹ in PRE: 13 [9, 15], in POST: 9 [8, 9], p = 0.01). None of the other variables were significantly different between and within groups. Under our experimental conditions, skin incision did not affect respiratory variables. Administration of pre- versus postoperative phenylbutazone and morphine did not influence recovery quality, HR, f_R, or animal behavior.

Electrochemotherapy of colorectal liver metastases--an observational study of its effects on the electrocardiogram

Background

Electrochemotherapy (ECT) is a combined treatment in which high voltage electroporation (EP) pulses are used to facilitate the uptake of a chemotherapeutic drug into tumor cells, thus increasing antitumor effectiveness of the drug. The effect of ECT of deep-seated tumors located close to the heart on functioning of the heart has not been previously investigated. In this study, we investigate the effects of intra-abdominal ECT of colorectal liver metastases on functioning of the heart during the early post-operative care period.

Methods

For ECT high voltage EP pulses with amplitudes of up to 3000 V and 30 A were delivered in synchronization with electrical activity of the heart. Holter electrocardiographic (ECG) signals were obtained from 10 patients with colorectal liver metastases treated with ECT. ECG was recorded during the periods of 24 hours before and after the surgical procedure involving ECT. Four-hour long night-time ECG segments from both periods exhibiting the highest level of signal stationarity were analyzed and compared. Changes in several ECG and heart rate variability (HRV) parameters were evaluated.

Results

No major heart rhythm changes (i.e., induction of extrasystoles, ventricular tachycardia or fibrillation) or pathological morphological changes (i.e., ST segment changes) indicating myocardial ischemia were found. However, we found several minor statistically significant but clinically irrelevant changes in HRV parameters after ECT procedures: a decrease in median values of the mean NN interval, a decrease in the low-frequency and in the normalized low-frequency component, and an increase in the normalized high-frequency component.

Conclusions

Only minor effects of intra-abdominal ECT treatment on functioning of the heart were found. They were expressed as statistically significant but clinically irrelevant changes in heart rate and long-term HRV parameters and were as such not life-threatening to the patients. The nature of these changes is such that they can be attributed to the known effects of the drugs given to the patients in the post-operative care. Further investigation is still warranted to unambiguously resolve whether ECT with high voltage EP pulses applied in immediate vicinity of the heart is responsible for the observed effects.

Comparison of Surgical Pleth Index-guided Analgesia with Conventional Analgesia Practices in Children: A Randomized Controlled Trial

BACKGROUND

To compare surgical pleth index (SPI)-guided analgesia with conventional analgesia by evaluating intraoperative analgesic requirements, postoperative pain, and emergence agitation in children.

METHODS

This study was designed as a parallel, two-arm, double-blind, randomized controlled trial. Forty-five children undergoing elective adenotonsillectomy were randomly allocated to SPI-guided group (SPI-guided analgesia group, n = 21) or control group (conventional analgesia group, n = 24). Anesthesia was maintained with sevoflurane 2 to 3 vol% in 50% nitrous oxide and oxygen to achieve state entropy between 40 and 60. Intraoperative fentanyl 0.5 μg/kg was administered for the first event persisting 3 min and subsequent events persisting 5 min. An event was defined as an SPI over 50 (SPI-guided group) or a blood pressure or heart rate 20% above the baseline (control group). The primary outcome was intraoperative fentanyl requirement. Secondary outcomes included intraoperative sevoflurane consumption, postoperative emergence agitation and pain score, and postoperative rescue analgesic requirements.

RESULTS

Intraoperative fentanyl requirement was lower in SPI-guided group than in control group (0.43 ± 0.53 vs. 1.73 ± 0.59 μg/kg; P < 0.001). Intraoperative sevoflurane consumption was similar. The proportion of patients with high emergence agitation scores (4 to 5) was greater in SPI-guided group (61.9 vs. 25.0%; P = 0.01). The postoperative pain score and rescue fentanyl consumption were higher in SPI-guided group (7 [4.5; 9] vs. 3 [2; 6.75]; P = 0.002; 0.50 ± 0.34 vs. 0.29 ± 0.30 μg/kg; P = 0.04).

CONCLUSIONS

As currently constructed, SPI does not appear to be valid in children. This may be due to both differences in blood vessel distensibility and baseline increased heart rates in children versus adults.

Assessing pain objectively: the use of physiological markers

Pain diagnosis and management would benefit from the development of objective markers of nociception and pain. Current research addressing this issue has focused on five main strategies, each with its own advantages and disadvantages. These encompass: (i) monitoring changes in the autonomic nervous system; (ii) biopotentials; (iii) neuroimaging; (iv) biological (bio-) markers; and (v) composite algorithms. Although each strategy has shown areas of promise, there are currently no validated objective markers of nociception or pain that can be recommended for clinical use. This article introduces the most important developments in the field and highlights shortcomings, with the aim of allowing the reader to make informed decisions about what trends to watch in the future.

Pulse perfusion value predicts eye opening after sevoflurane anaesthesia: an explorative study

The variables measured in modern pulse oximetry apparatuses include a graphical pulse curve and a specified perfusion value (PV) that could be a sensitive marker for detecting differences in sympathetic activity. We hypothesized that there is a correlation between a reduction of PV and the time to eye opening after general anaesthesia. The objective was to investigate whether PV can predict eye opening after sevoflurane anaesthesia. Prospective, explorative clinical study included 20 patients, ASA physical status 1 or 2, at Skåne University Hospital, Lund, Sweden, from November 2012 to January 2013 scheduled for elective breast tumour surgery. A general anaesthesia was delivered with inhalation of oxygen, nitrous oxide and sevoflurane anaesthesia to a depth of 1.2 minimal alveolar concentration. Sevoflurane inspiratory and expiratory concentrations were measured. Bispectral index monitoring, PV as measured by pulse oximeter, heart rate and carbon dioxide were registered at before anaesthesia, 15 min after induction (at 1.2 minimal alveolar concentration), at end of surgery and at eye opening at the end of anaesthesia. PV values were lower before anaesthesia and at eye opening compared to at 15 min after induction and at end of surgery (P < 0.05). The reduction of PV between end of surgery and eye opening was 0.76. We conclude that the pulse oximeter PV could be a useful variable to assess the timing of recovery, in terms of eye opening after a general anaesthesia.

Time-varying spectral analysis revealing differential effects of sevoflurane anaesthesia: non-rhythmic-to-rhythmic ratio

BACKGROUND

Heart rate variability (HRV) may reflect various physiological dynamics. In particular, variation of R-R peak interval (RRI) of electrocardiography appears regularly oscillatory in deeper levels of anaesthesia and less regular in lighter levels of anaesthesia. We proposed a new index, non-rhythmic-to-rhythmic ratio (NRR), to quantify this feature and investigated its potential to estimate depth of anaesthesia.

METHODS

Thirty-one female patients were enrolled in this prospective study. The oscillatory pattern transition of RRI was visualised by the time-varying power spectrum and quantified by NRR. The prediction of anaesthetic events, including skin incision, first reaction of motor movement during emergence period, loss of consciousness (LOC) and return of consciousness (ROC) by NRR were evaluated by serial prediction probability (PK ) analysis; the ability to predict the decrease of effect-site sevoflurane concentration was also evaluated. The results were compared with Bispectral Index (BIS).

RESULTS

NRR well-predicted first reaction (PK  > 0.90) 30 s ahead, earlier than BIS and significantly better than HRV indices. NRR well-correlated with sevoflurane concentration, although its correlation was inferior to BIS, while HRV indices had no such correlation. BIS indicated LOC and ROC best.

CONCLUSIONS

Our findings suggest that NRR provides complementary information to BIS regarding the differential effects of anaesthetics on the brain, especially the subcortical motor activity.

On the analysis of fingertip photoplethysmogram signals

Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses.

Changes in the photoplethysmogram with tracheal intubation and remifentanil concentration

Autonomic sympathetic activation, for instance following noxious stimuli, decreases the size and flattens the shape of the photoplethysmographic peripheral pulse waveform. We report a simple measure of the waveform shape, the ratio of mean-to-peak wave amplitude, for measuring nociception level during general anaesthesia. Fifty participants, anaesthetised with propofol and remifentanil, were randomly allocated to one of three different remifentanil effect-site concentrations (1, 3 and 5 ng.ml(-1) ). Tracheal intubation increased the mean (SD) plethysmographic ratio from 0.38 (0.06) to 0.48 (0.04), p = 2.6 × 10(-16) . The mean (SD) ratios following intubation at remifentanil effect-site concentrations of 1 ng.ml(-1) , 3 ng.ml(-1) and 5 ng.ml(-1) , were 0.49 (0.03), 0.48 (0.03) and 0.45 (0.04), respectively. Remifentanil therefore suppressed changes in the mean-to-peak ratio caused by tracheal intubation (p = 0.006). The ratio of the mean-to-peak plethysmographic amplitude may represent a simple measure of the balance of autonomic sympathetic and parasympathetic activity under general anaesthesia, and its performance following intubation was significantly different from peak amplitude (p = 0.046).

Correlation of surgical pleth index with stress hormones during propofol-remifentanil anaesthesia

Eighty patients undergoing elective ear-nose-throat surgery were enrolled in the present study to investigate the relationship between surgical pleth index (SPI) and stress hormones (ACTH, cortisol, epinephrine, norepinephrine) during general anaesthesia which was induced and maintained with propofol and remifentanil using a target-controlled infusion. The study concluded that the SPI had moderate correlation to the stress hormones during general anaesthesia, but no correlation during consciousness. Furthermore, SPI values were able to predict ACTH values with high sensitivity and specificity.

Wavelet transform cardiorespiratory coherence detects patient movement during general anesthesia

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). We have assessed the algorithm's ability to detect movement events (indicative of patient response to nociception) in 39 pediatric patients receiving general anesthesia. Sixty movement events were recorded during the 39 surgical procedures. Minimum and average WTCRC were calculated in a 30 second window surrounding each movement event. We used a 95% significance level as the threshold for detecting nociception during patient movement. The 95% significance level was calculated relative to a red noise background, using Monte Carlo simulations. It was calculated to be 0.7. Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.

Discriminant analysis for anaesthetic decision-making: an intelligent recognition system for epidural needle insertion

BACKGROUND

Incorrect placement of epidural catheters causes medical complications. We used linear discriminant analysis (LDA) to develop an intelligent recognition system (i-RS) in order to guide epidural placement and reduce physician error.

METHODS

We analysed real-time dual-wavelength fibreoptic data recorded from the end of an epidural needle in a live porcine model. Two categories of tissue layers were necessary for correct placement of catheter: epidural space and ligamentum flavum. The data were tested using linear, quadratic and logistic parametric analysis to identify which method could distinguish the two anatomical structures.

RESULTS

LDA was the best fit for our model. There was ∼80% sensitivity and specificity for correct anatomical identification. Error rates based on cross-validation were 17.0% for the epidural space and 18.6% for ligamentum flavum. Error rates were greater with the 532 nm compared with 650 nm wavelength.

CONCLUSIONS

The sensitivity and specificity of LDA for identifying the correct anatomical structure was similar to a physician who is an expert in epidural placement. Overall performance of an i-RS could be improved by expanding the database for decision-making and adding a category of uncertainty. This would reduce complications caused by incorrect epidural placement.

Increased variation of the response index of nociception during noxious stimulation in patients during general anaesthesia

OBJECTIVE

Analgesia is an important part of general anaesthesia, but no direct indicators of nociceptive-anti-nociceptive balance have been validated in detail. The Response Index of Nociception (RN) is a multiparameter approach which combines photoplethysmographic waveform (PPG), State Entropy (SE), Response Entropy (RE), and heart rate variability (HRV). We aimed at evaluating RN during general anaesthesia; especially we wanted to compare pre- and post-index values of certain noxious stimuli to the average index values. Our assumption was that RN could be a useful indicator of nociceptive-anti-nociceptive balance during the surgery.

METHODS

Sixty women undergoing gynaecological or breast surgery participated in the study. All patients had elective surgery and anaesthesia was maintained with propofol-remifentanil target controlled infusion. Neuromuscular blocking agent rocuronium was used at the beginning of the surgery. Electrocardiography (ECG), photoplethysmography (PPG) and electroencelophalography (EEG) were registered and extracted off-line. An index, reflecting amplitude and frequency of occurrence of abrupt increases ("peaks") in the RN was evaluated during surgery in general and around occurrences of predefined noxious stimuli in particular.

RESULTS

Fifty-four patients were eligible for analysis. Patient movement was associated with increased index values, both before and after the event. Post-event values of the index for intubation and skin incision were higher than its intra-surgery baseline, while pre-event values remained unchanged.

CONCLUSION

Changes in RN can be used to detect noxious stimuli during surgery. RN also predicted movement in our patients under propofol-remifentanil anaesthesia.

The Association between Intraoperative Electroencephalogram-Based Measures and Pain Severity in the Post-Anaesthesia Care Unit

This observational study aimed to identify simple electroencephalogram indices of inadequate intraoperative opioid-mediated nociceptive blockade and to compare these indices with routinely used clinical predictors of severe postoperative pain in adults. Intraoperative trend and waveform data (electrocardiogram, pulse oximetry and electroencephalogram) were collected, pain intensity in the post-anaesthesia care unit was quantified using an 11-point Verbal Rating Score, and opioid administration was recorded. Using the initial post-anaesthesia care unit Verbal Rating Score as the primary endpoint, the relationship between five possible explanatory variables – surgery type, depth of volatile anaesthesia (minimum alveolar concentration), electroencephalogram signs (state entropy, spindle-like activity and delta-band power) and estimated end-of-operation effect-site morphine concentrations – was examined. One hundred and thirteen patients were recruited, with 94 included in the final clinical and electroencephalogram data analysis. Fifty-two patients had moderate or severe pain (Verbal Rating Score >5). State entropy was lower (46.5±2.9 vs 43.1±1.9, P=0.04) and spindle-like activity higher (0.42±0.03 vs 0.50±0.02, P=0.03) in the moderate/severe pain group. These findings suggest that there is a modest association between electroencephalogram measures near the end of surgery and the severity of postoperative pain.

Optimizing intravenous drug administration by applying pharmacokinetic/pharmacodynamic concepts

This review discusses the ways in which anaesthetists can optimize anaesthetic-analgesic drug administration by utilizing pharmacokinetic and pharmacodynamic information. We therefore focus on the dose-response relationship and the interactions between i.v. hypnotics and opioids. For i.v. hypnotics and opioids, models that accurately predict the time course of drug disposition and effect can be applied. Various commercial or experimental drug effect measures have been developed and can be implemented to further fine-tune individual patient-drug titration. The development of advisory and closed-loop feedback systems, which combine and integrate all sources of pharmacological and effect monitoring, has taken the existing kinetic-based administration technology forwards closer to total coverage of the dose-response relationship.