Wearable electronic devices for chronic pain intensity assessment: A systematic review

Wearable electronic devices are a convenient solution to pain intensity assessment as they can provide continuous monitoring for more precise medication adjustments. However, there is little evidence regarding the use of wearable electronic devices for chronic pain intensity assessment. Our primary objective was to examine the physiologic parameters used by wearable electronic devices for chronic pain intensity assessment. We initially inquired PubMed, CINAHL, and Embase for studies evaluating the use of wearable electronic devices for chronic pain intensity assessment. We updated our inquiry by searching on PubMed, Embase, Scopus, and Google Scholar. English peer-reviewed studies were included, with no exclusions based on time frame or publication status. Of 348 articles that were identified on the first inquiry, 8 fulfilled the eligibility criteria. Of 179 articles that were identified on the last inquiry, only 1 fulfilled the eligibility criteria. We found articles evaluating wristbands, smartwatches, and belts. Parameters evaluated were psychomotor and sleep patterns, space and time mobility, heart rate variability, and skeletal muscle electrical activity. Most of the studies found significant positive associations between physiological parameters measured by wearable electronic devices and self-reporting pain scales. Wearable electronic devices reliably reflect physiologic or biometric parameters, providing a physiological correlation for pain. Early stage investigation suggests that the degree of pain intensity can be discerned, which ideally will reduce the bias inherent to existing numeric/verbal scales. Further research on the use of these devices is vital.

Emergence EEG pattern classification in sevoflurane anesthesia

OBJECTIVE

Significant spectral electroencephalogram (EEG) pattern characteristics exist in individual patients during the re-establishment of consciousness after general anesthesia. However, these EEG patterns cannot be quantitatively identified using commercially available depth of anesthesia (DoA) monitors. This study proposes an effective classification method and indices to classify these patterns among patients.

APPROACH

Four types of emergence EEG patterns were identified based on the EEG data set from 52 patients undergoing sevoflurane general anesthesia from two hospitals. Then, the relative power spectrum density (RPSD) of five frequency sub-bands of clinical interest (delta, theta, alpha, beta and gamma) were selected for emergence state analysis. Finally, a genetic algorithm support vector machine (GA-SVM) was used to identify the emergence EEG patterns. The performance was reported in terms of sensitivity (SE), specificity (SP) and accuracy (AC).

MAIN RESULTS

The combination of the mean and mode of RPSD in the delta and alpha band (P (delta)/P (alpha) performed the best in the GA-SVM classification. The AC indices obtained by GA-SVM across the four patterns were 90.64  ±  7.61, 81.79  ±  5.84, 82.14  ±  7.99 and 72.86  ±  11.11 respectively. Furthermore, the emergence time of the patients with EEG emergence patterns I and III increased as the patients' age increased. However, for patients with EEG emergence pattern IV, the emergence time positively correlates with the patients' age when they are under 50, and negatively correlates with it when they are over 50.

SIGNIFICANCE

The mean and mode of P (delta)/P (alpha) is a useful index to classify the different emergence EEG patterns. In addition, these patterns may correlate with an underlying neural substrate which is related to the patients' age. Highlights ► Four emergence EEG patterns were found in γ-amino-butyric acid (GABA)-ergic anesthetic drugs. ► A genetic algorithm combined with a support vector machine (GA-SVM) was proposed to identify the emergence EEG patterns. ► The relative power spectrum density (RPSD) was used as a feature to classify the emergence EEG patterns and good accuracy was achieved. ► The statistics shows that the emergence EEG patterns are age-related and may have value in assessing postoperative brain states.

Electromyographic activation reveals cortical and sub-cortical dissociation during emergence from general anesthesia

During emergence from anesthesia patients regain their muscle tone (EMG). In a typical population of surgical patients the actual volatile gas anesthetic concentrations in the brain (C_eMAC) at which EMG activation occurs remains unknown, as is whether EMG activation at higher C_eMACs is correlated with subsequent severe pain, or with cortical activation. Electroencephalographic (EEG) and EMG activity was recorded from the forehead of 273 patients emerging from general anesthesia following surgery. We determined C_eMAC at time of EMG activation and at return of consciousness. Pain was assessed immediately after return of consciousness using an 11 point numerical rating scale. The onset of EMG activation during emergence was associated with neither discernible muscle movement nor with the presence of exogenous stimulation in half the patients. EMG activation could be modelled as two distinct processes; termed high- and low-C_eMAC (occurring higher or lower than 0.07 C_eMAC). Low-C_eMAC activation was typically associated with simultaneous EMG activation and consciousness, and the presence of a laryngeal mask. In contrast, high-C_eMAC EMG activation occurred independently of return of consciousness, and was not associated with severe post-operative pain, but was more common in the presence of an endotracheal tube. Patients emerging from general anesthesia with an endotracheal tube in place are more likely to have an EMG activation at higher C_eMAC concentrations. These activations are not associated with subsequent high-pain, nor with cortical arousal, as evidenced by continuing delta waves in the EEG. Conversely, patients emerging from general anesthesia with a laryngeal mask demonstrate marked neural inertia-EMG activation occurs at a low C_eMAC, and is closely temporally associated with return of consciousness.

Emergence from general anesthesia and the sleep-manifold

The electroencephalogram (EEG) during the re-establishment of consciousness after general anesthesia and surgery varies starkly between patients. Can the EEG during this emergence period provide a means of estimating the underlying biological processes underpinning the return of consciousness? Can we use a model to infer these biological processes from the EEG patterns? A frontal EEG was recorded from 84 patients. Ten patients were chosen for state-space analysis. Five showed archetypal emergences; which consisted of a progressive decrease in alpha power and increase peak alpha frequency before return of responsiveness. The five non-archetypal emergences showed almost no spectral EEG changes (even as the volatile general anesthetic decreased) and then an abrupt return of responsiveness. We used Bayesian methods to estimate the likelihood of an EEG pattern corresponding to the position of the patient on a 2-dimensional manifold in a state space of excitatory connection strength vs. change in intrinsic resting neuronal membrane conductivity. We could thus visualize the trajectory of each patient in the state-space during their emergence period. The patients who followed an archetypal emergence displayed a very consistent pattern; consisting of progressive increase in conductivity, and a temporary period of increased connection strength before return of responsiveness. The non-archetypal emergence trajectories remained fixed in a region of phase space characterized by a relatively high conductivity and low connection strength throughout emergence. This unexpected progressive increase in conductivity during archetypal emergence may be due to an abating of the surgical stimulus during this period. Periods of high connection strength could represent forays into dissociated consciousness, but the model suggests all patients reposition near the fold in the state space to take advantage of bi-stable cortical dynamics before transitioning to consciousness.

Interfaces of sleep and anesthesia

In the past decades there has been an increasing focus on the relationship of sleep and anesthesia. This relationship bears on the fundamental scientific questions in anesthesiology, such as the mechanism of anesthetic-induced unconsciousness. However, given the increasing prevalence of sleep disorders in surgical patients, the interfaces of sleep and anesthesia are now a pressing clinical concern. This article discusses sleep and anesthesia from the perspective of phenotype, mechanism and function, with some concluding thoughts on the relevance to neuroanesthesiology.