Unpacking the Broad Landscape of Intraoperative Stressors for Clinical Personnel: A Mixed-Methods Systematic Review.

Purpose: The main goals of this mixed-methods systematic review are to identify what types of intraoperative stressors for operating room personnel have been reported in collected studies and examine the characteristics of each intraoperative stressor. Methods: With a systematic literature search, we retrieved empirical studies examining intraoperative stress published between 2010 and 2020. To synthesize findings, we applied two approaches. First, a textual narrative synthesis was employed to summarize key study information of the selected studies by focusing on surgical platforms and study participants. Second, a thematic synthesis was employed to identify and characterize intraoperative stressors and their subtypes. Results: Ninety-four studies were included in the review. Regarding the surgical platforms, the selected studies mainly focused on minimally invasive surgery and few studies examined issues around robotic surgery. Most studies examined intra-operative stress from surgeons' perspectives but rarely considered other clinical personnel such as nurses and anesthetists. Among seven identified stressors, technical factors were the most frequently examined followed by individual, operating room environmental, interpersonal, temporal, patient, and organizational factors. Conclusion: By presenting stressors as multifaceted elements affecting collaboration and interaction between multidisciplinary team members in the operating room, we discuss the potential interactions between stressors which should be further investigated to build a safe and efficient environment for operating room personnel.

Methods and Measures for Mental Stress Assessment in Surgery: A Systematic Review of 20 Years of Literature.

Real-time mental stress monitoring from surgeons and surgical staff in operating rooms may reduce surgical injuries, improve performance and quality of medical care, and accelerate implementation of stress-management strategies. Motivated by the increase in usage of objective and subjective metrics for cognitive monitoring and by the gap in reviews of experimental design setups and data analytics, a systematic review of 71 studies on mental stress and workload measurement in surgical settings, published in 2001-2020, is presented. Almost 61% of selected papers used both objective and subjective measures, followed by 25% that only administered subjective tools - mostly consisting of validated instruments and customized surveys. An overall increase in the total number of publications on intraoperative stress assessment was observed from mid-2010 s along with a momentum in the use of both subjective and real-time objective measures. Cardiac activity, including heart-rate variability metrics, stress hormones, and eye-tracking metrics were the most frequently and electroencephalography (EEG) was the least frequently used objective measures. Around 40% of selected papers collected at least two objective measures, 41% used wearable devices, 23% performed synchronization and annotation, and 76% conducted baseline or multi-point data acquisition. Furthermore, 93% used a variety of statistical techniques, 14% applied regression models, and only one study released a public, anonymized dataset. This review of data modalities, experimental setups, and analysis techniques for intraoperative stress monitoring highlights the initiatives of surgical data science and motivates research on computational techniques for mental and surgical skills assessment and cognition-guided surgery.

Automated tool detection with deep learning for monitoring kinematics and eye-hand coordination in microsurgery.

In microsurgical procedures, surgeons use micro-instruments under high magnifications to handle delicate tissues. These procedures require highly skilled attentional and motor control for planning and implementing eye-hand coordination strategies. Eye-hand coordination in surgery has mostly been studied in open, laparoscopic, and robot-assisted surgeries, as there are no available tools to perform automatic tool detection in microsurgery. We introduce and investigate a method for simultaneous detection and processing of micro-instruments and gaze during microsurgery. We train and evaluate a convolutional neural network for detecting 17 microsurgical tools with a dataset of 7500 frames from 20 videos of simulated and real surgical procedures. Model evaluations result in mean average precision at the 0.5 threshold of 89.5-91.4% for validation and 69.7-73.2% for testing over partially unseen surgical settings, and the average inference time of 39.90 ± 1.2 frames/second. While prior research has mostly evaluated surgical tool detection on homogeneous datasets with limited number of tools, we demonstrate the feasibility of transfer learning, and conclude that detectors that generalize reliably to new settings require data from several different surgical procedures. In a case study, we apply the detector with a microscope eye tracker to investigate tool use and eye-hand coordination during an intracranial vessel dissection task. The results show that tool kinematics differentiate microsurgical actions. The gaze-to-microscissors distances are also smaller during dissection than other actions when the surgeon has more space to maneuver. The presented detection pipeline provides the clinical and research communities with a valuable resource for automatic content extraction and objective skill assessment in various microsurgical environments.

EEG Emotion Recognition via Graph-based Spatio-Temporal Attention Neural Networks.

Emotion recognition based on electroencephalography (EEG) signals has been receiving significant attention in the domains of affective computing and brain-computer interfaces (BCI). Although several deep learning methods have been proposed dealing with the emotion recognition task, developing methods that effectively extract and use discriminative features is still a challenge. In this work, we propose the novel spatio-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of multi-column convolutional neural network and attention-based bidirectional long-short term memory. Moreover, we explore the inter-channel relationships of EEG signals via graph signal processing (GSP) tools. Our experimental analysis demonstrates that the proposed network improves the state-of-the-art results in subject-wise, binary classification of valence and arousal levels as well as four-class classification in the valence-arousal emotion space when raw EEG signals or their graph representations, in an architecture coined as GFT-STANN, are used as model inputs.

Prediction of Motor Imagery Performance based on Pre-Trial Spatio-Spectral Alertness Features.

Electroencephalogram (EEG) based brain-computer interfaces (BCIs) enable communication by interpreting the user intent based on measured brain electrical activity. Such interpretation is usually performed by supervised classifiers constructed in training sessions. However, changes in cognitive states of the user, such as alertness and vigilance, during test sessions lead to variations in EEG patterns, causing classification performance decline in BCI systems. This research focuses on effects of alertness on the performance of motor imagery (MI) BCI as a common mental control paradigm. It proposes a new protocol to predict MI performance decline by alertness-related pre-trial spatio-spectral EEG features. The proposed protocol can be used for adapting the classifier or restoring alertness based on the cognitive state of the user during BCI applications.

Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task.

Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectro-spatial features of the resting-state electroencephalograms (EEG). In this study, ten healthy volunteers have participated in fixed-sequence, varying-duration sessions of sustained attention to response task (SART) for over 100 minutes. A novel and adaptive cumulative vigilance scoring (CVS) scheme is proposed based on tonic performance and response time. Multiple linear regression (MLR) using feature relevance analysis has shown that average CVS, average response time, and variabilities of these scores can be predicted (p < 0.05) from the resting-state band-power ratios of EEG signals. Cross-validated neural networks also captured different associations for narrow-band beta and wide-band gamma and differences between the high- and low-attention networks in temporal regions. The proposed framework and these first findings on stable and significant attention predictors from the power ratios of resting-state EEG can be useful in brain-computer interfacing and vigilance monitoring applications.

Prediction of Response Time and Vigilance Score in a Sustained Attention Task from Pre-trial Phase Synchrony using Deep Neural Networks.

A real-time assessment of sustained attention requires a continuous performance measure ideally obtained objectively and without disrupting the ongoing behavioral patterns. In this work, we investigate whether the phasic functional connectivity patterns from short- and long-range attention networks can predict the tonic performance in a long Sustained Attention to Response Task (SART). Pre-trial phase synchrony indices (PSIs) from individual experiment blocks are used as features for assessment of the proposed average cumulative vigilance score (CVS) and hit response time (HRT). Deep neural networks (DNNs) with the mean-squared-error (MSE) loss function outperformed the ones with mean-absolute-error (MAE) in 4-fold cross-validations. PSI features from the 16-20 Hz beta sub-band obtained the lowest RMSE of 0.043 and highest correlation of 0.806 for predicting the average CVS, and the alpha oscillation PSIs resulted in an RMSE of 51.91 ms and a correlation of 0.903 for predicting the mean HRT. The proposed system can be used for monitoring performance of users susceptible to hypo- or hyper-vigilance and the subsequent system adaptation without implemented eye trackers. To the best of our knowledge, functional connectivity features in general and phase locking values in particular have not been used for regression models of vigilance variations with neural networks.

Stationary wavelet transform and higher order statistical analyses of intrafascicular nerve recordings.

Nerve signals were recorded from the sciatic nerve of the rabbits in the acute experiments with multi-channel thin-film longitudinal intrafascicular electrodes. 5.5 s sequences of quiescent and high-level nerve activity were spectrally decomposed by applying a ten-level stationary wavelet transform with the Daubechies 10 (Db10) mother wavelet. Then, the statistical distributions of the raw and subband-decomposed sequences were estimated and used to fit a fourth-order Pearson distribution as well as check for normality. The results indicated that the raw and decomposed background and high-level nerve activity distributions were nearly zero-mean and non-skew. All distributions with the frequency content above 187.5 Hz were leptokurtic except for the first-level decomposition representing frequencies in the subband between 12 and 24 kHz, which was Gaussian. This suggests that nerve activity acts to change the statistical distribution of the recording. The results further demonstrated that quiescent recording contained a mixture of an underlying pink noise and low-level nerve activity that could not be silenced. The signal-to-noise ratios based upon the standard deviation (SD) and kurtosis were estimated, and the latter was found as an effective measure for monitoring the nerve activity residing in different frequency subbands. The nerve activity modulated kurtosis along with SD, suggesting that the joint use of SD and kurtosis could improve the stability and detection accuracy of spike-detection algorithms. Finally, synthesizing the reconstructed subband signals following denoising based upon the higher order statistics of the subband-decomposed coefficient sequences allowed us to effectively purify the signal without distorting spike shape.