scholarly journals Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1733
Author(s):  
Somayeh B. Shafiei ◽  
Mohammad Durrani ◽  
Zhe Jing ◽  
Michael Mostowy ◽  
Philippa Doherty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Skill Assessment ◽  
Hand Gesture ◽  
Dominant Hand ◽  
Robot Assisted ◽  
Functional Brain Network ◽  
Eeg Data ◽  
Network Neuroscience

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.

scholarly journals Surgical skill assessment using machine learning algorithms

2021 ◽  
Vol 108 (Supplement_4) ◽  
Author(s):  
J L Lavanchy ◽  
J Zindel ◽  
K Kirtac ◽  
I Twick ◽  
E Hosgor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Movement Patterns ◽  
Surgical Skills ◽  
Machine Learning Algorithms ◽  
Skill Assessment ◽  
Surgical Skill ◽  
Surgical Skill Assessment

Abstract Objective Surgical skill is correlated with clinical outcomes. Therefore, the assessment of surgical skill is of major importance to improve clinical outcomes and increase patient safety. However, surgical skill assessment often lacks objectivity and reproducibility. Furthermore, it is time-consuming and expensive. Therefore, we developed an automated surgical skill assessment using machine learning algorithms. Methods Surgical skills were assessed in videos of laparoscopic cholecystectomy using a three-step machine learning algorithm. First, a three-dimensional convolutional neural network was trained to localize and classify the instruments within the videos. Second, movement patterns of the instruments were recorded over time and extracted. Third, the movement patterns were correlated with human surgical skill ratings using a linear regression model to predict surgical skill ratings automatically. Machine ratings were compared against human ratings of four board certified surgeons using a score ranging from 1 (poor skills) to 5 (excellent skills). Results Human raters and machine learning algorithms assessed surgical skills in 242 videos. Inter-rater reliability for human raters was excellent (79%, 95%CI 72-85%). Instrument detection showed an average precision of 78% and average recall of 82%. Machine learning algorithms showed an 87% accuracy in predicting good or poor surgical skills, when compared to human raters. Conclusion Machine learning algorithms can be trained to distinguish good and poor surgical skills with high accuracy. This work was published in Sci Rep 11, 5197 (2021). https://doi.org/10.1038/s41598-021-84295-6

Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby

2015 ◽  
pp. 53-80
Author(s):  
Duygun Erol Barkana ◽  
Engin Masazade
Keyword(s):  
Machine Learning ◽  
Emotional State ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Difficulty Level ◽  
Robot Assisted ◽  
Actual Performance ◽  
Sensory Data ◽  
Machine Learning Methods

Robot-assisted rehabilitation systems have shown to be helpful in neuromotor rehabilitation because it is possible to deliver interactive and repeatable sensorimotor exercise and monitor the actual performance continuously. Note that it is also essential to distinguish if subject finds the rehabilitation task difficult or easy, since the difficulty level of a task can yield different emotional state, such as excited, bored, over-stressed, etc., at each subject. It is important to adjust the difficulty level of the task to encourage the non-motivated subjects during the therapy. The physiological measurements, which can be obtained from the biofeedback sensors, can be used to estimate the subject's emotional state during the execution of the rehabilitation task. Machine learning methods can be used to classify the emotional state using the features of the biofeedback sensory data. This is explored in this chapter.

scholarly journals A Smart Device for the Prediction of Epileptic Seizures using Machine Learning Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 145-150
Author(s):  
Navya Ramakrishnan ◽  
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Learning Algorithms ◽  
Average Power ◽  
Epileptic Seizures ◽  
Machine Learning Algorithms ◽  
Smart Device ◽  
Daily Lives ◽  
Eeg Data ◽  
Unseen Data

More than 65 million people live with epilepsy. The unpredictable nature of epileptic seizures drastically increases the risk of injury, especially in daily activities such as walking or driving. The purpose of this project is to develop an accurate prediction device that utilizes raw EEG data for the prediction of epileptic seizures to alert patients of an oncoming seizure beforehand to escape dangerous situations. Using the raw EEG data, features were extracted by computing the average power spectral density of different brain waves after applying the Fast Fourier Transform. These features were used as the input dataset to the machine learning algorithms. Each model is tested with new unseen data using various metrics such as accuracy, precision, recall, and F1 score. The highest performing algorithm, Random Forest (RF) produced a prediction accuracy of 99.0% and a precision of 99.3%. Channel importance is calculated for the RF algorithm. This analysis helped to reduce the number of channels from 22 before feature importance to only 7 channels without significant hits to performance metrics. Using the RF algorithm, an embedded program is developed to run on a portable, low-power hardware device to predict the onset of a seizure. The hardware includes BeagleBone Black microcontroller running open-source software and a Bluetooth transmitter-receiver to transmit the prediction to smartphone devices. By reducing the number of EEG channels to 7 channels, the system is more convenient for a future wearable device. Hardware with the ability to predict epileptic seizures can save many patients from potentially dangerous situations such as driving or swimming. It can help many patients in their daily lives by removing the uncertainty and improving their quality of life.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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