Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

Gupta, Richa; Alam, Mansaf; Agarwal, Parul

doi:10.1155/2020/8860841

Cited by 50 publications

(17 citation statements)

References 32 publications

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“…A conventional approach for ECG signal classification tasks based on machine learning (ML) approaches is to use shallow models that take as input manually constructed features. The most common algorithms that are used in the stress classification task are Logistic Regression (LR) [ 21 , 22 ], Support Vector Machines (SVM) [ 23 , 24 ], Random Forests (RF) [ 25 ], Bayesian Networks (BN) [ 26 ], and K-Nearest Neighbors (KNN) [ 27 ]. In addition, in order to boost the overall performance, researchers frequently use hybrid techniques or model ensembles.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

Tzevelekakis

Stefanidi

Margetis

2021

Sensors

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Human stress is intricately linked with mental processes such as decision making. Public protection practitioners, including Law Enforcement Agents (LEAs), are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. In this respect, systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level information for their development, adaptation, and evaluation. To that end, our goal is to accurately detect and classify the level of acute, short-term stress, in real time, for the development of personalized, context-aware solutions for LEAs. Deep Neural Networks (DNNs), and in particular Convolutional Neural Networks (CNNs), have been gaining traction in the field of stress analysis, exhibiting promising results. Furthermore, the electrocardiogram (ECG) signals, have also been widely adopted for estimating levels of stress. In this work, we propose two CNN architectures for the stress detection and 3-level (low, moderate, high) stress classification tasks, using ultra short-term raw ECG signals (3 s). One architecture is simple and with a low memory footprint, suitable for running in wearable edge-computing nodes, and the other is able to learn more complex features, having more trainable parameters. The models were trained on the two publicly available stress classification datasets, after applying pre-processing techniques, such as data pruning, down-sampling, and data augmentation, using a sliding window approach. After hyperparameter tuning, using 4-fold cross-validation, the evaluation on the test set demonstrated state-of-the-art accuracy both on the 3- and 2-level stress classification task using the DriveDB dataset, reporting an accuracy of 83.55% and 98.77% respectively.

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Section: Related Workmentioning

confidence: 99%

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

Tzevelekakis

Stefanidi

Margetis

2021

Sensors

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“…Moreover, numerous optimization strategies are incorporated in training the ANFIS framework. In the research illustrated in [16], the PSO algorithm has been used for selecting the optimal features from the physiological dataset for predicting the stress level automatically. A firework algorithm (FA) is established by observing the explosion of fireworks, and it is mainly used in optimization problems that have complicated functions.…”

Section: A Optimization Approachesmentioning

confidence: 99%

An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data

2021

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Today's sedentary life leads to a plethora of lifestyle-related illnesses. This has led to the quest to predict diseases before they occur. In the past, research on stress prediction was carried out conventionally in a laboratory-based environment. However, recent studies are focusing on developing noninvasive ways to predict stress with the help of wearable devices. Generally, the models developed for stress prediction do not provide accurate results because the stress patterns are highly subjective and vary from person to person. Therefore, person-dependent models may achieve higher accuracies. These models, however, have to be trained with collected data over a comparatively longer period of time. In this paper, an Adaptive Neuro-Fuzzy Inference System aided Fire Works Grey Wolf Optimization (ANFIS-FWGWO) classification algorithm has been proposed for stress prediction. In particular, the proposed machine learning framework has been implemented to predict computer users' stress by using a sensor-integrated keyboard data. Various physiological parametric data were acquired during two different phases for the experimentation, and the received data was analyzed using an efficient machine learning framework. Specifically, the proposed framework encompasses various techniques such as data preprocessing for data smoothing and the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection algorithm for identifying the important features on the data set. From the experimental analysis, it is concluded that the ANFIS-FWGWO classification algorithm discriminates the stress subjects with a high degree of accuracy when compared with existing classification algorithms.

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“…e algorithm aims at constructing a hyperplane to separate positive and negative samples with the margin as large as possible. e SVM models are highly suitable for medium-size datasets and are less susceptible to overfitting than ANN models [46][47][48]. Given a training dataset x k , y k N k�1 with input data x k ∈ R n and corresponding class labels y k ∈ − 1, +1 { }, the SVM algorithm establishes a decision boundary so that the gap between classes is as large as possible.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Dinh

Hoang

Tran

2021

Applied and Environmental Soil Science

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Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.

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Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

Cited by 50 publications

References 32 publications

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

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