Effects of Fatigue Based on Electroencephalography Signal during Laparoscopic Surgical Simulation

Abstract

Background Following recent advances in technology, there is a growing interest in studying fatigue based on electrophysiological signals as a means of monitoring brain activity. While some existing works relate fatigue to performance, others consider the two as independent entities. Therefore, we must explore this intricate issue, particularly in laparoscopic training, for the sake of patient safety. Objective This paper explores and evaluates effects of fatigue on efficiency and accuracy based on laparoscopic surgical training using Electroencephalography (EEG) signal. Materials and Methods 20 college students performed peg transfer task on laparoscopic simulator, with real-time recording of EEG signals for each subject. To monitor degree of fatigue, a real-time fatigue monitoring system based on fatigue analysis algorithm was designed through the use of EEG in alpha (α) and theta (θ) rhythms. We designed data acquisition and fatigue analysis modules based on MATLAB platform. BrainLink was used to record EEG signals and send them to personal computer wirelessly via Bluetooth. While artifacts from the captured EEG signals were removed using Blind Source Separation (BSS), α and θ rhythms were extracted using wavelet analysis. Fatigue was evaluated based on Regression Model and Mahalanobis Distance (DC), and its threshold was determined from the experimental results using Receiver Operating Characteristic (ROC) curve analysis. Results Completion time and number of errors behaved like a decreasing function during the first few trials while increasing afterwards with the increasing of perceived fatigue level. The results indicate that learning curve of the subjects is increasing until 13th trials when they have attained maximum learning benefits and decreases afterwards due to fatigue. Conclusion Regression analysis shows that there are significant learning and fatigue effects when peg transfer task in the training is repeated in a series of trials. However, for the training to be effective and efficient, there should be monitoring during the training to observe where in the learning curve a trainee gains maximum learning benefits. Furthermore, fatigue is a significant indicator of efficiency and accuracy in terms of completion time and errors, respectively.

Detection of Vigil And Fatigue States During The Execution of Laparoscopic Tasks Based On EEG Patterns

“…In [39], it was found that stress (analyzed with alpha band records) is a factor that negatively in uences the execution of surgical practices. In [40], authors analyzed fatigue in surgical practices, by repeating the peg transfer task. They concluded that effectiveness in surgical practice is not determined by the time taken to perform an operation, but by the absence of errors.…”

Frontal theta brain activity varies as a function of surgical experience and task error

“…39 40 This behavior is the product of a distributed network of neural circuitry that takes a complex sequence of action selection and planned motor execution plan and refines over time to ensure smooth and seemingly automatic performance. 41 42 However, there have been very few investigations into the neural processes linking brain and behavior in the surgical domain, [43][44][45][46][47][48][49][50][51] with technological constraints limiting the ability to probe the neural underpinnings of surgical performance.…”

Using Electroencephalography (EEG) Power Responses to Investigate the Effects of Ambient Oxygen Content, Safety Shoe Type, and Lifting Frequency on the Worker’s Activities

“…These electrical signals are a combination of brain rhythms at different frequency levels falling into differing frequency, including delta waves (<4 Hz), theta waves (4-7 Hz), alpha waves (7)(8)(9)(10)(11)(12), beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma waves [5]. Studies have shown that delta activities are high during sleep while theta index activities are high on the early phase of sleepiness [6].…”

“…However, the detection of fatigue has not been applied to manual working, such as lifting, pulling, and carrying, which today accounts for the five primary causes of injury [23]. Previous work that has been carried out in the discipline has been central to the medical caretakers and drivers, indicating a research gap [24,25].…”