scholarly journals Comparison of Non-linear and Linear Models of Single Channel EEG in patients and normal subjects

2019 ◽  
Author(s):  
Gu ZhuoJun ◽  
Huang ZhiQiang ◽  
Zhu Xiao ◽  
Shi ShenXun
Keyword(s):  
Support Vector Regression ◽  
Linear Models ◽  
Single Channel ◽  
Psychiatric Patients ◽  
Normal Subjects ◽  
Support Vector ◽  
Eeg Signals ◽  
Principle Component ◽  
Principle Component Regression ◽  
Non Linear

AbstractThis article examines the possibility of using non-linear models(Support Vector Regression) to model the single channel EEG signals from psychiatric patients and a group of normal participants, to predict psychology trait ratings, like attention, anxiety, alertness, fatigue, sleepiness and depression. It used linear models as benchmarks, and the results showed non-linear models outperformed the benchmarks, as well as more advanced linear methods, like principle component regression. It is thus concluded that using single channel in practical situations to monitor these traits would be possible.

scholarly journals Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application

2018 ◽  
Vol 11 (6) ◽  
pp. 3717-3735 ◽  
Author(s):  
Alessandro Bigi ◽  
Michael Mueller ◽  
Stuart K. Grange ◽  
Grazia Ghermandi ◽  
Christoph Hueglin
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Low Cost ◽  
Multivariate Linear Regression ◽  
Pollution Level ◽  
Support Vector ◽  
Non Linear ◽  
Linear Algorithms ◽  
Urban Sites

Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240028 ◽  
Author(s):  
EE PING NG ◽  
TEIK-CHENG LIM ◽  
SUBHAGATA CHATTOPADHYAY ◽  
MURALIDHAR BAIRY
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Recurrence Quantification Analysis ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

scholarly journals Retrievals for the Rainfall Rate over Land Using Special Sensor Microwave Imager Data during Tropical Cyclones: Comparisons of Scattering Index, Regression, and Support Vector Regression

2012 ◽  
Vol 13 (5) ◽  
pp. 1567-1578 ◽  
Author(s):  
Chih-Chiang Wei ◽  
Jinsheng Roan
Keyword(s):  
Support Vector Regression ◽  
Tropical Cyclones ◽  
Single Channel ◽  
The Philippines ◽  
Rainfall Rate ◽  
Precipitation Forecast ◽  
Support Vector ◽  
Brightness Temperatures ◽  
Microwave Imager ◽  
Scattering Index

Abstract Tropical cyclones, also known as typhoons or hurricanes, are among the most devastating events in nature and often strike the western North Pacific region (including the Philippines, Taiwan, Japan, Korea, China, and others). This paper focuses on addressing the rainfall retrieval problem for quantitative precipitation forecast during tropical cyclones. In this study, Special Sensor Microwave Imager (SSM/I) data and Water Resources Agency (WRA) measurements of Taiwan were used to quantitatively estimate precipitation over the Tanshui River basin in northern Taiwan. Various retrievals for the rainfall rate over land are compared by five methods/techniques. They are the single-channel regression, multichannel linear regressions (MLR), scattering index over land approach (SIL), support vector regression (SVR), and the proposed SIL–SVR. This study collected 70 typhoons affecting the studied watershed over the past 12 years (1997–2008). The measurements of the SSM/I satellite comprise the brightness temperatures at 19.35, 22.23, 37.0, and 85.5 GHz. Overall, the results showed the approaches using the SVR and conjoined SVR and SIL performed better than regression and SIL methods according to their performances of the root-mean-square error (RMSE), bias ratio, and equitable threat score (ETS).

scholarly journals Support vector regression is an improvement for principle component analysis

2021 ◽  
Vol 23 (06) ◽  
pp. 1699-1715
Author(s):  
Mohamed, A. M. ◽  
◽  
Abdel Latif, S. H ◽  
Alwan, A. S. ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principle Component Analysis ◽  
Mean Squared Error ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Functions ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Principle Component

The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.

scholarly journals Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models

2022 ◽  
Vol 2161 (1) ◽  
pp. 012053
Author(s):  
B P Ashwini ◽  
R Sumathi ◽  
H S Sudhira
Keyword(s):  
Random Forest ◽  
Travel Time ◽  
Linear Models ◽  
Public Transit ◽  
Regression Trees ◽  
Support Vector ◽  
Learning Models ◽  
Random Forest Regression ◽  
The Public ◽  
Non Linear

Abstract Congested roads are a global problem, and increased usage of private vehicles is one of the main reasons for congestion. Public transit modes of travel are a sustainable and eco-friendly alternative for private vehicle usage, but attracting commuters towards public transit mode is a mammoth task. Commuters expect the public transit service to be reliable, and to provide a reliable service it is necessary to fine-tune the transit operations and provide well-timed necessary information to commuters. In this context, the public transit travel time is predicted in Tumakuru, a tier-2 city of Karnataka, India. As this is one of the initial studies in the city, the performance comparison of eight Machines Learning models including four linear namely, Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator Regression, and Support Vector Regression; and four non-linear models namely, k-Nearest Neighbors, Regression Trees, Random Forest Regression, and Gradient Boosting Regression Trees is conducted to identify a suitable model for travel time predictions. The data logs of one month (November 2020) of the Tumakuru city service, provided by Tumakuru Smart City Limited are used for the study. The time-of-the-day (trip start time), day-of-the-week, and direction of travel are used for the prediction. Travel time for both upstream and downstream are predicted, and the results are evaluated based on the performance metrics. The results suggest that the performance of non-linear models is superior to linear models for predicting travel times, and Random Forest Regression was found to be a better model as compared to other models.

scholarly journals Comprehensive Modeling and Molecular Docking of Phosphodiesterase Inhibitors

2021 ◽  
Author(s):  
zahra khosravi ◽  
elham baher ◽  
sajad gharaghani ◽  
salma ehsani
Keyword(s):  
Molecular Docking ◽  
Linear Models ◽  
Phosphodiesterase Inhibitors ◽  
Docking Study ◽  
Projection Algorithm ◽  
Support Vector ◽  
Molecular Docking Study ◽  
Qsar Study ◽  
Qsar Studies ◽  
Non Linear

Abstract In the present study, the biological activity of some pharmaceutical molecules was investigated and predicted by using quantitative structure-activity relationship (QSAR) studies and molecular docking. The aim of this study is to apply QSAR and molecular docking methods to predict and calculate the half maximal inhibitory concentration (IC50) of phosphodiesterase (PDE) 10A. To apply QSAR method at first multiple linear regression (MLR), genetic algorithm (GA), and successive projection algorithm (SPA) were used to select the best descriptors related to PDE 10A inhibitory activity. The selected descriptors were then applied as inputs to construct MLR and a non-linear support vector machine (SVM). Also we used molecular docking method to extract the descriptors and then by using MLR and SVM methods a linear and non-linear models developed respectively. Consequently, a comparison of the results of QSAR and docking study indicated that the non-linear SVM-SPA2 in QSAR study and SVM-MLR in molecular docking study had a much better prediction power than the other models. Finally, the Y-scrambling and cross-validation tests were used to evaluate the validity of the obtained model and the results of these tests indicates that the model is appropriate for using in prediction.

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