scholarly journals EEG beta-band spectral entropy predicts the effects of drug treatment on patients with herpes zoster

2019 ◽  
Author(s):  
Mengying Wei ◽  
Yuliang Liao ◽  
Jia Liu ◽  
Linling Li ◽  
Gan Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Herpes Zoster ◽  
Drug Treatment ◽  
Spectral Entropy ◽  
Beta Band ◽  
K Nearest Neighbors ◽  
Power Spectral ◽  
Machine Learning Model ◽  
Eeg Data ◽  
The Relationship

Abstract Background Medication is the main approach for early treatment of herpes zoster (HZ), but it could be ineffective in some patients. It is highly desired to predict the medication responses in order to control the degree of pain for HZ patients. The present study is aimed to elucidate the relationship between medication outcome and neural activity using electroencephalography (EEG) and to establish a machine learning model for early prediction of the medication responses from EEG. Methods We acquired and analyzed eye-closed resting-state EEG data 1-2 days after medication from 70 HZ patients with different drug treatment outcomes (measured 5-6 days after medicaiton): 45 medication-sensitive pain (MSP) patients and 25 medication-resistant pain (MRP) patients. EEG power spectral entropy (PSE) of each frequency band was compared at each channel between MSP and MRP patients, and those features showing sigificant difference between two groups were used to predict medication outcome with different machine learning methods. Results MSP patients showed significantly weaker beta-band PSE in the central-parietal regions than MRP patients. Based on these EEG PSE features and a k-nearest neighbors (k-NN) classifier, we can predicate the medication outcome with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity and an AUC of 0.85. Conclusion EEG beta-band PSE in the central-parietal region is predictive of the effectiveness of drug treatment on HZ patients, and it could potentially be used for early pain management and therapeutic prognosis.

EEG Beta-Band Spectral Entropy Can Predict the Effect of Drug Treatment on Pain in Patients With Herpes Zoster

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Mengying Wei ◽  
Yuliang Liao ◽  
Jia Liu ◽  
Linling Li ◽  
Gan Huang ◽  
...  
Keyword(s):  
Herpes Zoster ◽  
Drug Treatment ◽  
Spectral Entropy ◽  
Beta Band

scholarly journals Machine Learning Accurately Predicts Next Season NHL Player Injury Before It Occurs: Validation of 10,449 Player-Years from 2007-17

2020 ◽  
Vol 8 (7_suppl6) ◽  
pp. 2325967120S0036
Author(s):  
Audrey Wright ◽  
Jaret Karnuta ◽  
Bryan Luu ◽  
Heather Haeberle ◽  
Eric Makhni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Injury Risk ◽  
Medical Personnel ◽  
Operating Characteristics ◽  
K Nearest Neighbors ◽  
Predictive Algorithm ◽  
Preventative Intervention ◽  
Machine Learning Model ◽  
And Performance

Objectives: With the accumulation of big data surrounding National Hockey League (NHL) and the advent of advanced computational processors, machine learning (ML) is ideally suited to develop a predictive algorithm capable of imbibing historical data to accurately project a future player’s availability to play based on prior injury and performance. To the end of leveraging available analytics to permit data-driven injury prevention strategies and informed decisions for NHL franchises beyond static logistic regression (LR) analysis, the objective of this study of NHL players was to (1) characterize the epidemiology of publicly reported NHL injuries from 2007-17, (2) determine the validity of a machine learning model in predicting next season injury risk for both goalies and non-goalies, and (3) compare the performance of modern ML algorithms versus LR analyses. Methods: Hockey player data was compiled for the years 2007 to 2017 from two publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included: age, 85 player metrics, and injury history. A total of 5 ML algorithms were created for both non-goalie and goalie data; Random Forest, K-Nearest Neighbors, Naive Bayes, XGBoost, and Top 3 Ensemble. Logistic regression was also performed for both non-goalie and goalie data. Area under the receiver operating characteristics curve (AUC) primarily determined validation. Results: Player data was generated from 2,109 non-goalies and 213 goalies with an average follow-up of 4.5 years. The results are shown below in Table 1.For models predicting following season injury risk for non-goalies, XGBoost performed the best with an AUC of 0.948, compared to an AUC of 0.937 for logistic regression. For models predicting following season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared to an AUC of 0.947 for LR. Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season. As more player-specific data become available, algorithm refinement may be possible to strengthen predictive insights and allow ML to offer quantitative risk management for franchises, present opportunity for targeted preventative intervention by medical personnel, and replace regression analysis as the new gold standard for predictive modeling. [Figure: see text]

scholarly journals A Machine Learning Model for Recommending Restaurants based on User Ratings

2020 ◽  
Vol 9 (1) ◽  
pp. 732-736
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
K Nearest Neighbors ◽  
Processing Pipeline ◽  
Chinese Restaurant ◽  
Registered User ◽  
Machine Learning Model ◽  
History Of ◽  
Multiclass Svm ◽  
User Ratings

However, oftentimes people just search a restaurant by using word “restaurant”, while the word “restaurant” means differently to different individuals. For an Asian, it can mean a “Chinese restaurant” or “Thai restaurant”. How to correctly interpret search requests based on people’s preference is a challenge. Building a machine-learning model based on activity history of a registered user can solve this problem. The activity histories used by this research are reviews and ratings from users. This project introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each registered user. This project also defines an architecture, which uses the generated machine-learning models to support real-time personalized recommendations for restaurant searching and type of foods good at those recommended restaurants. Finally, this project aims to develop a good machine learning model, different collaborative filtering methodologies are considered to predict restaurants using user ratings. Slope One, k-Nearest Neighbors algorithm and multiclass SVM classification are some of the collaborating methodologies are going to consider in this project.

scholarly journals Prediction of preference and effect of music on preference: a preliminary study on electroencephalography from young women

2019 ◽  
pp. 1109-1119
Author(s):  
BÜLENT YILMAZ ◽  
CENGİZ GAZELOĞLU ◽  
FATİH ALTINDİŞ
Keyword(s):  
Support Vector ◽  
Purchasing Behavior ◽  
Forward Selection ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Brain Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Mean Differences ◽  
Sequential Forward Selection

Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19- channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.

scholarly journals MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT

2020 ◽  
Vol 13 (2) ◽  
pp. 89-96
Author(s):  
Muhammad Fachrie
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Political Parties ◽  
Multilayer Perceptron ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Machine Learning Model ◽  
Regional Elections ◽  
Election Commission

In this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of candidates in Regional Elections in Indonesia based on data taken from General Election Commission (KPU). The data consist of composition of political parties that support each candidate. The purpose of this research is to develop a Machine Learning model based on verified data provided by official institution to predict the victory of each candidate in a Regional Election instead of using social media data as in previous studies. The prediction itself simply a classification task between two classes, i.e. ‘win’ and ‘lose’. Several Machine Learning algorithms were applied to find the best model, i.e. k-Nearest Neighbors, Naïve Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest performance. We found in this research that Neural Networks with Multilayer Perceptron is the best model with 74.20% of accuracy.

Research on Evaluation of Mental Stress Level Using a Modified Duffing Oscillator

2016 ◽  
Author(s):  
Kentaro Miyago ◽  
Kenyu Uehara ◽  
Koji Mori ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Evaluation Method ◽  
Duffing Oscillator ◽  
Arousal Level ◽  
Beta Band ◽  
Research On Evaluation ◽  
Eeg Data ◽  
The Mathematical Model ◽  
The Relationship

The decrease of arousal level has led to serious accidents in driving and operation of machine. A system to accurately evaluate arousal level in real time is required to prevent such accidents. In this paper, we examined whether parameters of the mathematical model, which are determined in each time shifting window using EEG data could be used as a method to evaluate the arousal level. The modified Duffing oscillator was proposed as a mathematical model to perform simplified parameters calculation and identification. It was applied to EEG data, which is obtained by EEG acquisition experimentation in alternative repeat of relaxed and concentrated state. As a result of the parameters identification in theta band, alpha band, beta band, and 4–30 Hz band, it is shown that each parameter is repeating increase and decrease with time progress of each experiment. Especially, the relationship between the conventional arousal level and parameter B, which is the coefficient of the proportional term in the modified Duffing oscillator shows a significant correlation. It is suggested that parameter B of the modified Duffing oscillator would evaluate the arousal level. Since this evaluation method of arousal level does not require analyzing for each band, it can be used to calculate the arousal level in real time.

scholarly journals Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

2020 ◽  
Vol 8 (9) ◽  
pp. 232596712095340
Author(s):  
Bryan C. Luu ◽  
Audrey L. Wright ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Mark S. Schickendantz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Injury Risk ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Historical Data ◽  
Characteristic Curve ◽  
National Hockey League ◽  
K Nearest Neighbors ◽  
Machine Learning Model

Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR ( P < .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P < .0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season.

Relationship Between Wellness Index and Internal Training Load in Soccer: Application of a Machine Learning Model

2020 ◽  
pp. 1-9
Author(s):  
Enrico Perri ◽  
Carlo Simonelli ◽  
Alessio Rossi ◽  
Athos Trecroci ◽  
Giampietro Alberti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perceived Exertion ◽  
Training Load ◽  
Supervised Machine Learning ◽  
Training Time ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
The One ◽  
The Relationship

Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi) and WI measured the day after (WIMDi+1) (r = .72, P < .001). Additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and WI. Nevertheless, the higher accuracy of ordinal regression (39% [2%]) compared with the results obtained by baseline B1 (21% [1%]) demonstrated that the machine learning approach used in this study can predict the WI according to the TL performed the day before (MD<i). Conclusion: The machine learning technique can be used to predict the WI based on a targeted weekly TL. Such an approach may contribute to enhancing the training-induced adaptations, maximizing the players’ readiness and reducing the potential drops in performance associated with poor wellness scores.

scholarly journals The Classification of Elbow Extension and Flexion: A feature selection investigation

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 68-73
Author(s):  
Mohamad Ilyas Rizan ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Amirul Abdullah ◽  
Mohd Azraai Mohd Razman ◽  
Anwar P. P. Abdul Majeed
Keyword(s):  
Machine Learning ◽  
Information Gain ◽  
Advanced Technology ◽  
Modern World ◽  
K Nearest Neighbors ◽  
Rehabilitation Process ◽  
Machine Learning Model ◽  
Elbow Movement ◽  
The Time Domain ◽  
The Right

Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This research uses mechanomyography (MMG) and machine learning models to classify the elbow movement, extension and flexion of the elbow joint. The study will aid in the control of an exoskeleton for stroke patient's rehabilitation process in future studies. Five volunteers (21 to 23 years old) were recruited in Universiti Malaysia Pahang (UMP) to execute the right elbow movement of extension and flexion. The movements are repeated five times each for two active muscles for the extension and flexion motion, namely triceps and biceps. From the time domain based MMG signals, twenty-four features were extracted from the MMG before being classified by the machine learning model, namely k-Nearest Neighbors (k-NN). The k-NN has achieved the classification accuracy (CA) with 88.6% as the significant features are identified through the information gain approach. It may well be stated that the suggested process was able to classify the elbow movement well

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